AWS Partner Network (APN) Blog

Data-driven optimization and AI-powered orchestration for Nextflow with Fovus on AWS

Ragib Ahsan — Thu, 28 May 2026 18:41:04 +0000

By: Quang Minh Le, Founding Software Engineer
By: Sunny Sharma, Founding PMM, HPC & AI
By: Gokhul Srinivasan, Senior Partner Solutions Architect – AWS
By: Ragib Ahsan, AI Acceleration Architect II – AWS

Fovus

If you run Nextflow pipelines on Amazon Web Services (AWS), you’ve probably encountered a persistent inefficiency: static resource configurations that apply the same Amazon Elastic Compute Cloud (Amazon EC2) instance type uniformly across pipeline steps with fundamentally different hardware needs. Memory-bound alignment steps, single-threaded QC tools, I/O-saturating binary alignment/map (BAM) conversion, and embarrassingly parallel variant callers each share the same static resource definition. Lightweight steps are overprovisioned, more demanding steps fail and restart, and most of the Amazon EC2 instance catalog goes unused. In practice, static configurations can overspend by 70–85%. As sequencing costs have dropped from approximately $50,000 per genome in 2010 to under $200–$300 today (NHGRI), the volume of genomic data requiring computational processing has grown correspondingly. NHGRI estimates that genomic research now generates on the order of 2–40 exabytes of data annually, making computational processing the primary operational bottleneck. Pharmaceutical companies, genome centers, and clinical labs processing hundreds to thousands of samples per week feel this directly in research velocity, cost, and reproducibility.

In this post, you will see how Fovus optimizes each Nextflow process individually through per-process benchmarking and data-driven optimization, walk through benchmark results from nf-core/rnaseq and nf-core/sarek pipelines, and find out how to get started with a no cost pilot. Nextflow has emerged as the workflow language of choice for these workloads. Its containerized execution model, native AWS Batch integration, and the nf-core library of curated pipelines make it well-suited for cloud-scale genomics. Fovus, an AWS Software and HPC Competency Partner, addresses this by benchmarking each process individually and using the resulting data to assign each Nextflow process the Amazon EC2 instance type, memory configuration, parallel computing settings, and storage strategy it needs.

The challenge: Heterogeneous steps, static configs, and manual work

Fovus benchmarking of nf-core/rnaseq and nf-core/sarek reveals the spread of performance bottleneck types across their top processes:

nf-core/rnaseq process characteristics:

Process	Primary bottleneck	Parallel scalability
SALMON_INDEX	Compute and memory	High
SALMON_QUANT	Compute and memory	High
TRIMGALORE	Compute	High
PICARD_MARKDUPLICATES	Memory	Low
STAR_ALIGN	Memory	Mid
QUALIMAP_RNASEQ	Memory	Low
DUPRADAR	Compute and I/O	Low
RSEQC_READDUPLICATION	Memory	Low
RSEQC_READDISTRIBUTION	Compute	Low

nf-core/sarek (30 times Whole Genomic Sequencing (WGS)) process characteristics:

Process	Primary bottleneck	Parallel scalability
BASERECALIBRATOR	Compute	Low
BWAMEM1_MEM	Compute	High
CNNSCOREVARIANTS	Compute and I/O	High
CNVKIT_BATCH	Memory	Mid
CRAM_TO_BAM	I/O	High
DEEPVARIANT	Compute and I/O	High
ENSEMBLVEP_VEP	Compute	Mid
FASTP	Compute and I/O	High
FASTQC	Compute	Low
FREEBAYES	Compute	Low
GATK4_APPLYBQSR	Compute and memory	Low
GATK4_MARKDUPLICATES	Memory	Low
HAPLOTYPECALLER	I/O and memory	Low
MANTA_GERMLINE	Compute	High
MERGE_CRAM	Compute	Low
SAMTOOLS_REINDEX_BAM	Memory	Low
SAMTOOLS_STATS	Compute	Low
STRELKA_SINGLE	Compute	Mid
TIDDIT_SV	Compute and memory	High

A single static resource configuration can’t capture this variability. Fovus tackles this with benchmarking-data-driven optimization and AI-powered orchestration.

How Fovus works: Benchmark, optimize and run, continuously improve

Fovus integrates with Nextflow through nf-fovus, a lightweight Nextflow plugin that requires no code changes to your pipelines or containers. Your pipelines run unchanged – Fovus generates the Nextflow configuration file for nf-fovus and handles AWS orchestration.

Figure 1: Fovus integration workflow

Fovus uses AWS CloudFormation to deploy a managed HPC cluster within your own virtual private cloud (VPC) with a head node in a public subnet orchestrating compute across private subnets. Fovus dynamically selects the optimal Amazon EC2 instances for each process, sizing, storage, and parallel computing settings tailored per-process. A distributed file system built on Amazon Simple Storage Service (Amazon S3) with local SSD caching provides high-performance POSIX-compatible storage for I/O-intensive steps. Inputs and outputs flow through your Amazon S3 bucket.

You can optimize your pipelines in three steps with Fovus:

Figure 2: HPC strategy space exploration graph

Benchmark. Fovus auto-profiles each pipeline process individually against representative inputs—measuring bottlenecks, resource consumption, and the performance impacts of applicable HPC strategies on AWS. Benchmarking runs automatically at no charge with minimal setup.
Optimize and run. At run time, an AI optimization engine ingests the benchmark profiles and determines the optimal HPC strategy and configurations for minimizing time-cost per process. It selects Amazon EC2 instance types and sizes, Amazon Elastic Block Store (Amazon EBS) storage configurations, parallel computing settings, and Amazon EC2 Spot Instances or on-demand routing based on benchmarking data and real-time AWS instance pricing and availability dynamics. A per-run priority dial lets you balance cost against time. Running your Nextflow pipeline on AWS with Fovus requires a single command using the Fovus command line interface (CLI), or a few clicks in the web UI. There are no AWS Batch or AWS ParallelCluster environments to manage and no configuration to tune by hand.
Continuously improve. As AWS rolls out new infrastructure, Fovus automatically updates benchmarking profiles so your pipelines use the latest technologies. This requires no workflow changes and sustains time-cost optimality as infrastructure evolves.

Figure 3: Fovus deployment architecture on AWS

Key capabilities:

Per-process HPC strategy optimization: Benchmarking-data-driven, not human-guessed
Memory checkpointing: Fovus Memguard snapshots process state at intervals and upon Spot Instance interruption. Interrupted runs auto-resume from the last checkpoint with Spot-to-Spot failover, making Spot Instances practical for long-running processes that previously required on-demand.
Intelligent Spot Instance deployment: Automatically uses Spot Instances with Spot-to-Spot failover and availability-aware strategy optimization to improve cost efficiency.
Hybrid strategy distribution: Ranks strategies by time and cost. Workloads automatically spill from first-optimal to second-optimal choices as capacity fills, with optional multi-Region sweeps for processes with limited I/O footprint.
Customer guardrails: Configurable constraints on the scope of HPC strategy optimization, AWS Regions, and spend applied at the infrastructure layer.
Distributed file system: POSIX-compatible distributed file system using local SSD as a transparent Amazon S3 cache, provisioned per-process based on measured I/O demand.

Benchmarking results

We ran nf-core/rnaseq and nf-core/sarek end-to-end on AWS with Fovus, benchmarked using automated profiling and executed with optimized per-process configurations. The following tables compare on-demand and Spot Instance costs.

Results: nf-core RNA-seq

CPU-only, test_full, eight samples, approximately 100 million paired-end reads for each sample.

Run configuration	AWS cost with Fovus	Walltime (h) with Fovus
On-demand—minimize cost	$14.42	9h 12m 36s
Spot Instance with memory checkpointing—minimize cost	$5.72	8h 15m 36s

As shown in the preceding table, the cost is approximately $0.70 per sample on Spot Instances—a 70–85% cost savings compared to single-config cloud deployments, providing 3–7 times higher dollar efficiency.

Results: nf-core Sarek – 30x WGS (CPU-Only*)

Test full germline (30 times WGS, single nucleotide/ondel multi-caller/structural variant/copy number variant/annotation):

Run configuration	AWS cost with Fovus	Walltime (h) with Fovus
On-demand—minimize cost	$12.45	9h 39m 25s
Spot Instance with memory checkpointing—minimize cost	$6.15	9h 29m 2s

Test full normal-tumor pair:

Run configuration	AWS cost with Fovus	Walltime (h) with Fovus
On-demand—minimize cost	$5.30	4h 26m 7s
Spot Instance with memory checkpointing—minimize cost	$2.22	5h 1m 11s

The Spot Instance run matches on-demand wall time almost exactly (9h 29m compared to. 9h 39m) while cutting cost by more than half. Memory checkpointing makes this possible: interruptions are absorbed invisibly and full Spot Instance savings are captured with no reliability tradeoff.

Note: These results reflect a CPU-only strategy. Fovus supports GPU-accelerated tools, which can deliver further improvements on wall time.

Deployment: Bring your own cloud (BYOC)

Fovus deploys directly within your AWS account: Your data, compute, and pipeline execution remain inside your existing cloud environment. Fovus is designed to support HIPAA compliance requirements and data sovereignty and aligns with your organization’s AWS security and governance policies, with no data leaving your account boundary. Pricing is consumption-based—a fee on the AWS spend managed by Fovus, with no license fees, per-seat charges, or upfront commitments. The result is lower cost or higher efficiency than unoptimized workloads, with savings that offset the Fovus cost.

Getting started: No-cost pilot

Fovus provides a pilot workspace to benchmark your pipelines and generate optimized Nextflow configurations for immediate testing on AWS. For teams already running Nextflow, migration is a single file replacement—swap your existing nextflow.config with the Fovus-generated one. No pipeline modifications, no container changes, no workflow rewrites.

Conclusion

Running a heterogeneous Nextflow pipeline under a single shared resource configuration leaves money and time behind. With Fovus, each process has its own optimized HPC strategy and configurations, determined based on benchmarking data and executed automatically through AI-powered orchestration. The results: Approximately $0.70 per sample for nf-core/rnaseq and $6.15 for a full 30 times germline WGS on Spot Instances (CPUs only). For teams on cloud or on-premises clusters, Fovus demonstrates that you can achieve lower costs and faster results by running HPC workloads on AWS.

Resources

Fovus – Free benchmarking and pilot workspace
AWS Marketplace – Fovus listing
nf-core Pipeline Catalog
Amazon EC2 Spot Instances
NHGRI DNA Sequencing Costs Data – Source for sequencing cost figures
NHGRI Genomic Data Science Fact Sheet –- Source for genomic data volume estimates

Note: Fovus performance and cost figures are from actual runs of nf-core/rnaseq (test_full, 8 samples, approximately 100 million PE reads) and nf-core/sarek (30 times WGS, SN/Indel multi-caller/SV/CNV/annotation). AWS costs reflect Amazon EC2 and Amazon EBS charges at time of execution. Results vary by pipeline, input data, AWS Region, and prevailing Spot Instances pricing. Speed and cost comparisons are based on unoptimized single-config cloud deployments.

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Fovus – AWS Partner Spotlight

Fovus is an AWS Advanced Technology Partner that provides an AI-powered serverless HPC platform that simplifies and optimizes cloud HPC for enterprises. It provides intelligent, scalable, affordable supercomputing power at your fingertips to accelerate scientific discoveries and engineering breakthroughs, helping you achieve more with less.

Contact Fovus | Partner Overview | AWS Marketplace

Accelerate vendor evaluation with AWS Specializations and AWS Marketplace

Kelly Costlow — Wed, 27 May 2026 14:46:54 +0000

By: Soonam Kurian, Global Tech Leader, AWS Specializations – AWS
By: Kelly Costlow, Global Program Manager, AWS Specializations – AWS

AWS Specializations and AWS Marketplace Vendor Insights make your vendor evaluation more efficient by surfacing pre-validated technical capabilities and transparent, up-to-date security postures, allowing faster, more confident vendor selection decisions aligned to your unique requirements.

Traditional vendor assessment takes 8–12 weeks across procurement, security, and technical teams. You spend 6 weeks—50% of evaluation time—researching partners. Your teams manually collect compliance documentation, assess architectures, and validate controls. Retail, healthcare, and financial services organizations face additional challenges verifying solutions meet security standards and industry frameworks such as Payment Card Industry Data Security Standard (PCI DSS), Health Insurance Portability and Accountability Act (HIPAA), or AWS System and Organization Controls (SOC) 2.

A multilayered validation framework addresses these challenges through objective, standardized assessments. In this post, you’ll learn how two complementary mechanisms, AWS Specializations and AWS Marketplace Vendor Insights, can help you perform vendor evaluation due diligence more efficiently.

AWS Specializations: Finding the right partner

Identifying AWS Partners with specialized expertise for your specific business use cases is a significant challenge. AWS Specializations helps you identify partner solutions vetted through objective, Amazon Web Services (AWS) defined technical validations across more than 115 solution areas spanning industries, use cases, workloads, and AWS services. This helps you focus your evaluation on partners who have already met rigorous, standardized criteria. A total of 87% of AWS customers rank AWS Specializations as a top three selection criterion, and 96% expect partner directory listings to have undergone validation (AWS Customer Survey, 2024, n=556).

When a partner holds an AWS Specialization, it means they have passed objective technical validations AWS defines for that specific domain, provided examples of successful customer engagements, and demonstrated a solution validated by AWS. To earn and maintain a Specialization, partners must also sustain a strong team of trained and certified technical individuals. Partners undergo rigorous assessment across organizational practices, solution architecture, customer success demonstration, and domain-specific requirements. For example, the AWS Retail Competency evaluates retail-specific use cases such as personalization engines, and the AWS Data and Analytics Competency assesses data lake architectures, real-time processing capabilities, and analytics optimization.

AWS prioritizes the following aspects when evaluating partner capabilities:

Architecture excellence – Assess multi-AZ deployment, scalability mechanisms, disaster recovery strategies, and native AWS service integration patterns. Validate solutions against the AWS Well-Architected Framework, examining AWS service leverage, integration patterns, deployment automation, monitoring capabilities, and issue response procedures.
Security and compliance – Verify that solutions meet industry standards and implement encryption, access controls, and issue management practices. Assess IAM policies, encryption implementations, network segmentation, and monitoring capabilities against compliance frameworks.
Proven customer success – Demonstrate production deployments with quantifiable business outcomes. Qualify partner solutions through customer case studies demonstrating real-world effectiveness, including architecture diagrams, AWS services utilized, and measurable outcomes.
Domain expertise – Evaluate domain-specific expertise for AWS Specializations through Validation Checklists (VCLs), tailored assessment frameworks unique to each Specialization area. VCLs define specific technical criteria, solution requirements, and best practices that partners must demonstrate for their target domain.

AWS imposes a rigorous Specialization validation process that verifies partners meet proven technical standards and customer success criteria, giving you a strong, objective foundation to build on. The due diligence required to identify the right partner for your specific workloads and business outcomes depends on your unique requirements.

Your benefits

Being able to use AWS Specializations to identify AWS Partners with the right expertise for your specific business use cases offers several advantages:

Efficient partner discovery – You identify partners with AWS validated technical expertise across specific domains, focusing your evaluation on prequalified candidates rather than starting from scratch.
Objective validation baseline – You use the AWS standardized assessment work as a credible starting point, directing your team’s effort toward evaluating fit for your specific requirements.
Targeted partner discovery – You quickly locate partners with proven domain expertise that directly aligns with your specific business requirements.

These benefits accelerate procurement cycles. AWS Specialization criteria give you a reliable signal of partner capability, freeing your teams to focus evaluation time on what matters most: fit for your unique workloads, scale, and business context.

The following graphic illustrates the evaluation aspects and customer benefits of using AWS Specializations to find the right partner.

Figure 1: AWS Specializations: Finding the right partner

AWS Marketplace Vendor Insights: Security transparency

AWS Marketplace is a curated digital catalog where you find, buy, deploy, and manage third-party software, data, and services that that you need to build solutions and run your businesses.

AWS Marketplace Vendor Insights streamlines software risk assessments to support your procurement process and help verify solutions meet your standards. After you discover products addressing specific business requirements, identify products with available Vendor Insights by filtering results under Refine results and choosing Security profiles. Then access detailed security information from the Vendor Insights section on the product details page. You can use Vendor Insights to monitor product security profiles in near real time from a single console, reducing IT security approval time from months to hours.

Vendor Insights compiles security and compliance information from three complementary sources:

Industry standard audit reports – ISO 27001, AWS System and Organization Controls (SOC) 2 Type II, Payment Card Industry Data Security Standard (PCI DSS), and other compliance certifications provide third-party validated evidence of security controls and compliance adherence.
Self-assessment and Consensus Assessment Initiative Questionnaire (CAIQ) – Seller-reported security practices and controls.
AWS Audit Manager – Automated evidence collection from seller production environments using AWS Config rules provides continuous, real-time security posture monitoring.

This multilayered approach provides continually updated security postures rather than static, point-in-time assessments, giving your security teams the current, structured information they need to complete assessments efficiently.

Your benefits

The up-to-date information provided by Vendor Insights offers you these benefits:

Rapid assessments – Complete software risk assessments in hours instead of weeks.
Continuous monitoring – Track post-procurement security controls with automated notifications for compliance certificate expirations and data residency changes.
Centralized visibility – View a single dashboard across your AWS Marketplace software purchases.

Real-world application

A multinational retailer with more than 500 stores across North America seeks a customer data platform (CDP) to consolidate customer interactions across ecommerce, mobile apps, point-of-sale systems, and loyalty programs. The retailer faces regulatory requirements that include PCI DSS Level 1 for payment data.

Evaluation process

Agent mode combines the AWS Marketplace product catalog with specialized knowledge from trusted third parties through a conversational interface purpose-built for software procurement. The retailer uses agent mode’s conversational interface to describe CDP requirements in natural language or upload requirement documents, receiving instant product recommendations. The retailer then selects from preset tasks, such as comparing specific products. The product comparison feature creates summaries of overall fit for each of the top three product recommendations based on customer reviews, features, and AWS integration capabilities. The following screenshot shows the product comparison screen agent mode.

Figure 2: Agent mode for AWS Marketplace product discovery

After the retailer identifies promising CDP candidates, they access Vendor Insights directly from product detail pages to monitor security profiles and compliance artifacts in near real time from a single console. The Overview tab shows the product’s security certifications. The retailer requests subscription to Vendor Insights to view the detailed security profile, including data security, access management, and business resiliency.

The following screenshot shows a partner overview in Vendor Insights.

Figure 3: AWS Marketplace Vendor Insights overview

The retailer uses AWS Partner Solutions Finder to evaluate partners’ technical capabilities through the AWS Specializations they hold. The partner profile highlights AWS Specializations such as Competencies and Service Validations the partner attained, AWS Partner programs they participate in, and validated solutions and customer case studies.

Figure 4: AWS Partner Solution Finder

For example, a retailer uses a combination of Vendor Insights and Partner Solution Finder to identify three vendors that we’ll call Vendor A, Vendor B, and Vendor C.

Vendor A holds AWS Retail and AWS Data and Analytics Competencies. Vendor Insights shows SOC 2 Type II, ISO 27001, and PCI DSS Level 1 certifications current within 90 days. Solution resources available on AWS Marketplace demonstrate Amazon Kinesis for real-time ingestion, Amazon Simple Storage Service (Amazon S3) with encryption for data lake storage, Amazon Redshift for analytics, and AWS Lambda for event processing. Four retail customer case studies show implementations for companies with similar scale, including a national grocery chain achieving 360-degree customer view with 99.9% uptime.

Vendor B lists AWS Data and Analytics Competency but lacks retail-specific validation. Vendor Insights profile shows SOC 2 Type II, but PCI DSS certification expired 120 days prior. Customer case studies demonstrate financial services and healthcare expertise, but no retail implementations.

Vendor C displays AWS Retail Competency without AWS Data and Analytics Competency. Vendor Insights shows current compliance certifications, but solution documentation reveals limited real-time processing capability, relying primarily on batch ETL processes.

The AWS Marketplace agent also generates purchase proposals with insights gathered throughout the discovery process, supporting you to confidently engage pre-vetted solutions without investing time and resources in redundant technical evaluations.

The retailer selects Vendor A based on dual Specialization alignment, current compliance posture, proven retail success, and technical architecture fit. Complementary vendor evaluation mechanisms from AWS reduced selection time from 10 weeks to 3 weeks (a 70% reduction).

How the framework works together

The integrated validation framework combines rigorous partner validation through Specializations, AI-powered discovery through AWS Marketplace agent mode, and continuous security monitoring through Vendor Insights. Together, these mechanisms streamline weeks of manual vendor research into an efficient evaluation workflow, giving your teams objective, structured inputs so you can focus evaluation effort on assessing fit for your unique requirements. The evaluation workflow consists of these steps:

Identify your vendor selection need.
Define your evaluation criteria.
Search AWS Marketplace.
Perform evaluations:
a. Evaluate the partner profile.
b. Evaluate Vendor Insights.
Compare the vendors’ technical capabilities and security profiles.
Make a decision. If the vendor meets your criteria, proceed to step 7. If the vendor doesn’t meet your criteria, return to step 3.
Select the vendor that meets your requirements.
Continue to monitor security risk profile of the selected vendor.

These steps are illustrated in the following graphic.

Figure 5: Vendor evaluation framework

Conclusion

AWS validates partners through rigorous assessment of specialized domain expertise, technical capabilities, and transparent security postures. AWS Specializations signal that a partner has passed objective, domain-specific technical validations defined by AWS, completed AWS verified customer engagements, and maintained a certified and trained technical team. Vendor Insights provides continuous security transparency through standardized dashboards and current compliance attestations.

These mechanisms make your vendor evaluation more efficient, surfacing credible, structured information faster so your teams can direct effort where it matters most: evaluating which partner best fits your specific workloads, requirements, and business context.

Use Vendor Insights to evaluate vendors and take advantage of these benefits:

Accelerated evaluation cycles – AWS validated partner criteria and centralized security profiles reduce time spent on baseline research, which means your teams can focus on fit assessment.
Increased confidence – Objective, AWS defined validation criteria provide a credible foundation; 87% of AWS customers actively use Specializations in selection processes.
Accelerated procurement – Standardized information facilitates rapid comparison across multiple vendors.
Maintained compliance – Continual monitoring maintains ongoing security posture visibility.

Get started today

Start by exploring AWS Marketplace agent mode to discover solutions for your business requirements through natural conversation. Then evaluate partners using AWS Specializations badging on AWS Partner Solution Finder and AWS Marketplace Vendor Insights security profiles to identify vendors meeting your technical and security requirements. Finally, filter specific specializations using AWS Partner Solution Finder to discover the right partners and their solutions by industries, use cases, AWS Partner Program participation, AWS service usage, and geographical presence.

Accenture and AWS accelerate data transformation with agentic AI

Rajdeep Banerjee — Tue, 26 May 2026 18:58:56 +0000

By: Mark Stanger, Lead Architect, Semantic Layer – Accenture
By: Aditi Pendse, Lead Data Architect, Agentic Data Platforms & Cloud Modernization – Accenture
By: Sandeep Chatterjee, Product Lead, Agentic Data Discovery – Accenture
By: Rajdeep Banerjee, Senior Partner Solutions Architect – AWS

Accenture

Enterprises that treat data as a strategic asset will define the next era of competitive advantage. The opportunity is significant: agentic AI can autonomously discover, migrate, govern, and consume data at a pace and scale that was impossible only 2 years ago. Accenture and Amazon Web Services (AWS) are accelerating enterprise data transformation with three agentic AI solutions now available in AWS Marketplace, purpose-built to make enterprise data ready for generative AI and agentic applications.

Organizations are actively working to scale generative AI solutions. The Accenture Pulse of Change finds that nearly nine in ten organizations plan to increase AI investment in 2026, and most view it as a driver of revenue growth. Yet only one in five report redesigning end-to-end processes with AI at the core. Gartner projected that at least 30% of generative AI projects will be abandoned after proof of concept by end of 2025. This gap can be put down to data readiness rather than AI capability. Data remains fragmented across platforms, poorly documented, and locked in legacy formats that AI models can’t effectively use.

Accenture Agentic Data Discovery for AWS, Accenture Agentic Data Modernization for AWS, and Accenture Agentic Semantic Layer for AWS address this gap directly. Together, they transform how organizations discover, migrate, govern, and consume enterprise data, closing the readiness gap that stands between AI ambition and AI impact.

Agentic solutions across the data lifecycle

The three solutions map to four phases of data transformation: assess and discover, capture data for AI, curate data for AI, and consume data with AI. Organizations don’t need to follow these phases sequentially. You can start wherever you are and run multiple workstreams simultaneously, whether that means discovering current data estates, migrating legacy platforms, or building a semantic layer for AI consumption. The solutions are complementary but independent. Accenture Agentic Data Discovery identifies and prioritizes data assets. Accenture Agentic Data Modernization migrates and transforms them. Accenture Agentic Semantic Layer federates access to data wherever it resides, across AWS and other platforms, making it queryable by AI agents and applications without requiring data centralization.

Accenture Agentic Data Discovery for AWS

Most organizations don’t fully understand their own data landscape. Reports are undocumented, lineage is unknown, and shadow IT creates blind spots that block AI initiatives before they start.Accenture Agentic Data Discovery deploys intelligent agents that scan metadata across databases, schemas, and extract, transform, and load (ETL) code to build a comprehensive, continuously updated inventory of your data estate. The agents classify data assets as active or inactive, enabling prioritization for migration and modernization efforts. The solution delivers automated discovery across your entire data landscape, uncovering hidden patterns and delivering inventory, volumetrics, and object distribution insights. Interactive visualizations provide comprehensive views of your data estate, including sensitive data detection for personally identifiable information (PII) and protected health information (PHI). A conversational AI interface means data stewards and architects can query the data landscape in plain English, no SQL required, with agents analyzing and responding in real time. An end-to-end lineage viewer traces data flows across on-premises and cloud-based systems, with progressive graph expansion showing upstream and downstream dependencies.An example Accenture customer using Data Discovery achieved approximately 50% inventory rationalization based on inactive and one-time inventory identification. They consolidated approximately 25% of their inventory through detailed similarity analysis across data, code, and business intelligence assets. And they performed comprehensive technical analysis of over 1,400 reports with end-to-end lineage to support modernization planning.

Accenture Agentic Data Modernization for AWS

Migrating legacy data platforms to AWS is a resource-intensive phase of cloud transformation. Organizations face months of manual code conversion, data mapping, and testing, with quality and governance often addressed as afterthoughts.

Accenture Agentic Data Modernization deploys purpose-built agents that automate three workstreams. For code migration, Data Engineer agents handle the full lifecycle of legacy code transformation, from ingesting source ETL scripts, stored procedures, and SQL dialects to chunking, reviewing, converting, and optimizing code for target platforms. Supported conversions include Oracle, Teradata, Db2, Informatica PowerCenter, and DataStage to Amazon Redshift, Amazon Athena, Amazon Simple Storage Service (Amazon S3) with Apache Iceberg, Snowflake, PySpark, and others. For data quality and governance, Data Quality Engineer agents discover and profile source data, define quality rules, validate data against those rules, and generate verification scripts. For automated testing, Data Testing agents translate business requirements into test cases, generate test scripts, run them against target environments, and validate results, eliminating the manual testing bottleneck that typically extends migration timelines.

Agents are built on Amazon Bedrock AgentCore, enabling accelerated deployment within your AWS environment. By applying Agentic Data Modernization, organizations are modernizing legacy platforms, improving confidence in migrated data through automated testing and quality agents, and unlocking scalable data marketplaces backed by AWS led co-innovation.

Accenture Agentic Semantic Layer for AWS

As organizations modernize their data estates on AWS, a new challenge emerges how to make that data truly consumable for analytics, generative AI, and agentic applications without introducing new silos, duplication, or governance risk. The Agentic Semantic Layer for AWS addresses this challenge by creating a governed, AI-ready abstraction that sits above enterprise data, transforming raw, distributed datasets into business‑meaningful knowledge that can be safely and easily consumed.The solution accelerates decision-making by translating complex, distributed data into trusted, business-ready insights. Business users can query data in natural language and receive consistent, governed answers with minimal technical team involvement, while AI teams benefit from reusable, standardized metrics and semantics. This enables high-value use cases such as executive self-service analytics, domain-specific copilots, autonomous reporting, and AI-driven decision support across structured and unstructured data.At its core, the Agentic Semantic Layer establishes a shared semantic foundation that bridges technical data structures and business intent. Using AI-assisted ontology creation, it captures enterprise concepts, relationships, and metrics in a formalized model that reflects how the business thinks about its data. This semantic model becomes the backbone for analytics, natural language querying, and agentic workflows, providing all consumers with a consistent and trusted understanding of enterprise information.The solution is designed around a “data in place” philosophy. Rather than forcing organizations to centralize or duplicate data, it federates access across sources wherever they reside, whether in Amazon Redshift, Amazon S3, Amazon Athena, operational databases, or third-party platforms such as Snowflake. A knowledge graph engine continuously enriches the semantic layer with metadata, lineage, and contextual relationships. As new sources are onboarded, the layer adapts automatically, preserving alignment between physical data assets and business semantics and creating a living knowledge fabric that supports both traditional business intelligence (BI) and advanced agentic AI scenarios.Governance and trust are built in by design. Enterprise access controls, data classification, and policy enforcement are integrated directly into semantic resolution, which means users and AI agents only access what they’re authorized to see. Organizations can confidently expose data through natural language interfaces and semantic APIs while maintaining regulatory compliance and data sovereignty.An example Accenture client using the Agentic Semantic Layer deployed over 300 data products across 25 domains, achieved a 600% throughput improvement for data sourcing teams, and realized 30–40% data infrastructure savings through smart data productization while building readiness for future agentic AI workloads.

Conclusion

The future of enterprise data management is agentic, intelligent, and automated. Organizations that adopt these capabilities can gain competitive advantages in an AI-driven marketplace.AWS and Accenture invite enterprise leaders to explore how these agentic data solutions can accelerate your data transformation journey. Whether you’re beginning to assess your data landscape, planning a major migration, or building the semantic infrastructure for AI applications, these solutions provide the capabilities, governance, and scale that enterprise success requires.

Connect with the Accenture AWS Business Group to learn how we can help you transform your data estate into a strategic asset that powers innovation, drives insights, and enables the agentic AI applications of tomorrow.

The Accenture and AWS Partnership advantage

The collaboration between Accenture and AWS brings together complementary strengths that uniquely position these solutions for enterprise success. AWS provides comprehensive cloud infrastructure capabilities, with AI services through Amazon Bedrock and robust data infrastructure. Accenture contributes more than 16 years of partnership experience, having migrated over 110,000 workloads to AWS and delivered more than 4,100 joint projects across global clients.

This partnership has been recognized with multiple 2025 AWS awards, including Global Consulting Partner of the Year and Global GenAI Partner of the Year. More importantly, it combines the technical innovation of AWS with Accenture’s deep understanding of enterprise transformation challenges, regulatory requirements, and industry-specific needs.

Accenture – AWS Partner spotlight

Accenture is an AWS Premier Tier Services Partner and managed service provider (MSP) that provides end-to-end solutions to migrate to and manage operations on Amazon Web Services (AWS). By working with the Accenture AWS Business Group (AABG), a strategic collaboration by Accenture and AWS, organizations can accelerate the pace of innovation to deliver disruptive products and services.
Contact Accenture | Partner Overview | AWS Marketplace

Generative AI using TiDB and Amazon Bedrock

Akshata Raghunatha — Tue, 26 May 2026 16:39:48 +0000

By: Henrique Leandro, Head of Global Solutions Architect – PingCAP
By: Akshata Raghunatha, Partner Solutions Architect – AWS
By: Satish Subramanian, Manager, Partner Solutions Architect – AWS

PingCAP

The shift from conversational chatbots to autonomous, goal-driven systems is changing how we think about application architecture. Instead of predictable, human-paced interactions, we’re beginning to design for software that acts continuously — planning, evaluating, revising, and executing without waiting for a user to choose “Submit.”

In this blog post, we explore how TiDB Cloud, an AWS Partner Network (APN) Partner, addresses the unique data layer challenges introduced by autonomous AI agents. We look at how TiDB Cloud’s distributed architecture, powered by TiDB Cloud’s serverless compute-storage separation model, supports these emerging patterns on AWS — particularly when paired with Amazon Bedrock for reasoning and embedding workloads using the TiDB console.

The challenge: when the user isn’t human

In these systems, the database is no longer responding to occasional human queries. Instead, it responds to agents that generate SQL automatically and create temporary schemas. These agents expect immediate access to both transactional records and contextual history.

Autonomous agents don’t behave like customers signing in during business hours. They work in loops — planning, executing, evaluating results, and trying again. A single instruction such as “optimize supply chain logistics” can unfold into thousands of internal operations. Agents can create temporary tables to isolate experiments, branch schemas to test alternative strategies, and persist large volumes of intermediate reasoning state.

For platform leaders, the question isn’t whether agents will increase load — it’s how to support unpredictable, burst-heavy workloads without losing control of cost or operational stability. Architectures built around fixed capacity and manual tuning start to feel misaligned when the user is a machine (AI agent) operating continuously.

From a data perspective, that behavior surfaces several practical challenges:

Metadata management becomes non-trivial. If agents routinely create and discard schemas, the underlying database must handle that churn without degrading performance.
Concurrency patterns become less predictable. Workloads can move from idle to highly parallel in seconds, especially if multiple agents begin exploring variations simultaneously.
Cost becomes tightly coupled to elasticity. When compute must remain provisioned as a contingency, idle time becomes expensive. In autonomous systems, unused capacity isn’t only inefficient — it directly affects economic viability.

TiDB Cloud: a data layer designed for elastic systems

TiDB Cloud approaches these challenges with a distributed architecture that separates compute from storage. In this model, data persists in Amazon Simple Storage Service (Amazon S3) while compute resources scale independently. This separation changes the economics and behavior of the database layer.

Independent scaling of storage and compute

Because storage and compute are decoupled, compute resources can scale dynamically. They scale up during periods of heavy agent activity and scale down when workloads subside. Data remains durable in object storage, while stateless processing nodes are allocated as needed.

For agent-driven systems, this model aligns infrastructure costs more closely with actual usage. When activity slows, compute doesn’t need to remain fully provisioned. When activity spikes, additional capacity can be introduced without re-architecting the system.

Branching as a first-class capability

Agents often need isolated environments to test ideas. If an agent restructures a schema or rewrites a data model, that experimentation should be done in a way that doesn’t jeopardize production.

TiDB Cloud supports serverless branching using copy-on-write techniques. A branch can be created quickly, allowing an agent or a developer to explore a hypothesis in isolation. After the task is complete, that branch can be discarded, or reset to sync with the latest parent state.

This brings database workflows closer to the way teams already treat application code: versioned, testable, and disposable when necessary.

Unifying transactional and hybrid search workloads

Autonomous agents rarely ask just one kind of question. Within a single reasoning chain, an agent may draw on transactional memory for state, retrieve related context through hybrid search, match against domain-specific keywords, and reference analytical summaries of recent data. Each of these is a different access pattern, and traditionally each has required a different system.

Instead of stitching together separate engines for vector search, full-text search, real-time analytics, and relational queries, TiDB Cloud integrates row-based storage for transactions, columnar storage for real-time analytics, and native vector and full-text search for hybrid retrieval.

This reduces architectural fragmentation and keeps state consistent across access patterns. The result is a database that acts as durable memory for the agent – searchable across modalities and transactionally accurate.

Reference architecture: TiDB Cloud and Amazon Bedrock

In practice, the integration works as follows:

Applications call Amazon Bedrock embedding models to convert documents and queries into vector representations.
TiDB stores those embeddings in native vector columns alongside the transactional data.
An orchestration layer, typically implemented using AWS Lambda, Amazon Elastic Container Service, or frameworks like LangChain, coordinates the workflow, retrieving relevant context using vector similarity search in TiDB.
The orchestration layer passes the retrieved context to an Amazon Bedrock text generation model, then writes the agent’s resulting decisions back to TiDB as structured transactions.

Figure 1 – Reference architecture for TiDB Cloud and Amazon Bedrock.

The architecture shown in the preceding figure consists of the following:

Presentation layer – End users: application interface for user queries and responses.
Application layer – Custom application: frontend and middleware orchestrating user interactions and API calls.
AI/ML services layer – Models:
- Amazon Bedrock text model: foundation model for response generation.
- Amazon Bedrock embeddings model: converts documents and queries to vector embeddings.
Vector database layer – TiDB: stores and retrieves vector embeddings for semantic search.
Storage layer – Amazon S3: document repository for source data.

Together, these layers separate reasoning from persistence while maintaining a cohesive data model.

Use cases

TiDB Cloud on AWS supports a broad range of autonomous agent workloads — from real-time fraud detection to multi-tenant software-as-a-service (SaaS) operations — and can be configured using the TiDB console, shown in the following screenshot. The following example use cases illustrate how teams are combining TiDB’s unified HTAP architecture with Amazon Bedrock to build production-ready AI systems across industries.

Figure 2 – TiDB console.

Conversational analytics for ecommerce

An ecommerce platform uses TiDB Cloud on AWS to store product catalogs, inventory, and order data. An autonomous agent powered by Amazon Bedrock translates natural language queries — such as “which products are trending but running low on stock?” — into SQL executed against TiDB’s HTAP engine. The agent combines real-time transactional data with analytical aggregations in a single query, eliminating the need for a separate data warehouse.

Fraud detection in financial services

A fintech company stores transaction records in TiDB Cloud on AWS. An agent uses Amazon Bedrock for reasoning and TiDB’s vector search to match transaction embeddings against known fraud signatures. The agent queries millions of transactions in real time (using OLTP) while running analytical pattern detection (using OLAP) — all within a single distributed database.

Multi-tenant SaaS operations

A SaaS platform uses TiDB Cloud’s distributed SQL to serve thousands of tenants. An autonomous agent provisions new tenant databases, monitors usage patterns, and identifies churn signals — all through SQL operations using TiDB on AWS. The serverless branching capability allows the agent to test schema changes in isolation before applying them to production. This TiDB blog post highlights how TiDB empowered Atlassian’s Forge platform to support a massive, multi-tenant architecture with over 3 million tables — all within a single cluster.

Live operations in gaming

A gaming company stores player profiles, match history, and in-game economy data in TiDB Cloud on AWS. An agent analyzes player engagement, detects anomalous behavior patterns using vector similarity search, and recommends live event configurations — querying millions of concurrent player records without manual sharding.

GenAI experimentation without heavy setup

Agentic architectures are still evolving. Teams often need room to prototype and iterate before committing to production-scale designs.

TiDB Cloud on AWS provides a managed, serverless environment that teams can use to experiment with branching, vector search, and SQL-driven agents without standing up complex infrastructure. This simplifies validating workload assumptions and refining architectural decisions before scaling further. This TiDB blog post highlights how TiDB Cloud enabled extreme write throughput and low-latency state reconstruction that a monolithic database couldn’t sustain for a successful public launch with a more than 2 million transaction waitlist within weeks.

Conclusion

The question isn’t whether autonomous agents will transform your architecture — it’s whether your database will be ready when they do. The teams building tomorrow’s AI systems are choosing infrastructure today.

By running TiDB Cloud on AWS and integrating with Amazon Bedrock, organizations can design a database layer that supports transactional accuracy, hybrid retrieval, real-time analytics, and rapid experimentation within a unified architecture. Instead of working around database constraints, architects can focus on shaping how autonomous systems behave, and how they evolve. Unlike architectures that require separate databases for transactions, analytics, and hybrid search, TiDB Cloud unifies these capabilities while maintaining the elasticity and cost efficiency autonomous systems demand.

Ready to build your agentic architecture? Explore TiDB Cloud on AWS Marketplace or contact PingCAP to discuss your specific use case.

PingCAP – AWS Partner Spotlight

PingCAP is an AWS Advanced Technology Partner that provides TiDB Cloud, a fully managed distributed SQL database designed for hybrid transactional and analytical processing (HTAP) workloads.

Contact PingCAP | Partner Overview | AWS Marketplace

Announcing the Regional 2026 AWS Partners of the Year for Latin America

Andrew Somera — Fri, 22 May 2026 16:09:00 +0000

By: Andrew Somera, Head of Partner Experiences

We are excited to announce the regional 2026 AWS Partners of the Year for Latin America. These annual awards recognize AWS Partner Network (APN) members who consistently deliver exceptional results and drive innovation for our customers.

Throughout 2026, our winning partners have demonstrated their expertise by delivering transformative cloud solutions and creating measurable business value through AWS technologies. They’ve successfully guided customers through complex migrations and helped organizations optimize their AWS environments for maximum impact.

The collaboration between AWS and our partners continues to be fundamental to customer success. These partnerships drive innovation and help customers take full advantage of AWS services to accelerate their growth.

Regional Awards are comprised of the following four awards:

Consulting Partner of the Year: Recognizes our top AWS Consulting Partners that have provided significant contributions related to revenue, launched opportunities, net new certified individuals, and AWS designations earned.
Rising Star Consulting Partner of the Year: Recognizes AWS Consulting Partners that have seen significant YoY growth in their business.
Technology Partner of the Year: Recognizes our top AWS Technology Partners that are using AWS to lower costs, increase agility, and innovate faster.
Rising Star Technology Partner of the Year: Recognizes AWS Technology Partners that have seen significant YoY growth in their business.

Please join us in congratulating our 2026 AWS Partner of the Year award winners!

Northern Latin America (NOLA) Winners

Consulting Partner of the Year – Escala24x7
Rising Star Consulting Partner of the Year – JumpCube
Technology Partner of the Year – Truora
Rising Star Technology Partner of the Year – DanaConnect

Southern Latin America (SOLA) Winners

Consulting Partner of the Year – Dinocloud
Rising Star Consulting Partner of the Year – Craftech
Technology Partner of the Year – Hackmetrix
Consulting Partner of the Year – Horizon

Mexico Winners

Consulting Partner of the Year – XalDigital
Rising Star Consulting Partner of the Year – Globaltask
Technology Partner of the Year – Cerby
Rising Star Technology Partner of the Year – EVOLVENX

Brazil Winners

Consulting Partner of the Year – Compass UOL
Rising Star Consulting Partner of the Year – A3 Data
Technology Partner of the Year – VTEX
Rising Star Technology Partner of the Year – Pier Cloud

How AWS partner scales success with Hiring Side of Innovation from AWS

Serena Kang — Wed, 20 May 2026 15:05:09 +0000

By: Serena Kang, Asia Pacific Japan Partner Enablement Manager – Amazon Web Services

Technology and industry trends are constantly evolving, and companies often struggle to adopt agile hiring strategies that can keep up. Many Amazon Web Services (AWS) partners face the same challenge, their rapid business growth often outpaces their internal recruiting capability, creating a gap between market ambition and operational readiness. The Hiring Side of Innovation (HSOI) program was born from this need to help partners bridge that divide by treating talent acquisition not as an administrative task, but as a strategic business lever.

HSOI is a structured strategic enablement program designed to help AWS partners build a high-velocity Internal Engine by transforming their hiring mechanisms and organizational culture into a driver for scalable business growth. Unlike one-off best practice sharing programs, the HSOI program is a results-oriented 3–6-month project where we dive deeply into the partner’s specific challenges to co-create measurable business impact and long-term operational excellence.

The program helps partners streamline their recruitment process, augmenting everything from job description optimization to technical candidate assessment using proven frameworks and AWS best practices. This initiative, which has supported AWS partners since 2023, has achieved measurable outcomes such as:

- 100% headcount target attainment
- 20%+ reduction in hiring cycle time
- 20%+ improvement in talent funnel conversion
- Incremental revenue contribution

In this post, you’ll discover how AWS helps partners scale their hiring innovation through SoftwareOne’s success story using the HSOI methodology, and how your organization can get involved.

Video: AWS Hiring Side of Innovation – SoftwareOne Story

The SoftwareOne journey

SoftwareOne, a global provider of cloud technology solutions and an Amazon Web Services (AWS) Premier Tier Services Partner, faced similar hurdles and embraced the Hiring Side of Innovation (HSOI) program to re-align their recruitment engine with their growth trajectory.

Over 6 months, SoftwareOne partnered with AWS to optimize their hiring process in a hands-on collaboration. The initiative moved away from intuition-based hiring and toward evidence-based decision-making. It involved envisioning workshops with SoftwareOne executives and the HR team to dive deeply into their specific challenges in hiring and talent management, explored solutions, and set up measurable goals such as headcount target attainment and cycle time reduction. This work resulted in an actionable, achievable roadmap to transform their hiring process.

The Software One team tested a new sourcing mechanism, ran new employee referral drives, and conducted talent market insight studies across multiple countries. “These sessions gave us fresh ideas to make existing initiatives more engaging,” says Priya Rajarathinam, regional talent acquisition leader at SoftwareOne. “Even our referral initiatives, already strong, got the extra benefits.”

Quantifiable efficiency gains

SoftwareOne reduced their overall hiring lifecycle time by approximately 25–40%. “We have reduced the overall lifecycle of hiring, which helps our teams to focus more on project delivery and customer success,” says Devendran Ramachandran, delivery head of digital workforce at SoftwareOne. This efficiency means their technical teams can focus on what matters most, which is delivering projects and innovation for customers.

From hiring culture to business impact

Partnering with AWS moved SoftwareOne from learning best practices to shaping them for themselves. The alignment between talent acquisition and business leadership meant everyone began speaking the same language regarding hiring.

This cultural shift supported their evolution as a strategic partner, eventually contributing to their entry into a strategic collaboration agreement with AWS. This multiyear commitment focuses on joint investment and coordinated go-to-market initiatives, particularly around AI and generative AI.

Conclusion

The HSOI program can help you turn recruitment into a measurable competitive advantage. By aligning talent strategy with business objectives, you can build a team that leads impactful outcomes such as revenue generation, competitive wins, and cost savings. For information on how you can start improving your talent strategy, you can connect with us by submitting this form today.

Accelerating supply chain AI with Kinaxis MCP on Amazon Bedrock AgentCore

Naresh Rajaram — Mon, 18 May 2026 16:56:49 +0000

By: Shriram Bidkar, Assistant Vice President, AWS Solutions – Genpact
By: Naresh Rajaram, Sr. Partner Solutions Architect – AWS

Genpact

The biggest barrier to supply chain AI isn’t the technology, it’s integration. Genpact’s Kinaxis MCP Server, built on Amazon Bedrock AgentCore, replaces months of custom API work with a single secure interface. One manufacturer achieved an estimated reduction in AI deployment time from 12 weeks to 3 days. A global retailer saw up to an 18% improvement in forecast accuracy. Discover how MCP standardization unlocks supply chain AI at scale.

A Fortune 500 consumer goods manufacturer wanted to use AI agents to support supply chain planning. Teams spent months building and securing custom API connections between the agents and their planning systems. Each new agent required additional integration work, driving up costs and delaying deployment. As a result, the engineering effort was consumed by integration tasks rather than supply chain optimization.

This scenario is the norm, not the exception. Enterprises adopting ML-based supply chain forecasts typically cut errors by 20–50%, releasing $20 to $40 million in working capital per $1 billion of revenue. The challenge isn’t acquiring AI, it’s connecting AI agents securely to the specialized systems that run modern supply chains. With industry leaders achieving up to a 30% reduction in inventory costs while improving service levels, organizations can’t afford multi-month integration projects. They need AI operational now.

Breaking down the integration barrier

Model Context Protocol (MCP) is changing this. APIs standardized how software systems communicate, now MCP is doing the same for AI agents—replacing dozens of custom integrations with a standard interface. With thousands of AI agents already built across Amazo n organizations since 2025, and enterprises managing supply chains spanning hundreds of millions of SKUs, the conditions are right for a breakthrough

When AI is applied effectively in supply chain planning and execution, organizations can unlock meaningful improvements in cost, inventory efficiency, and customer service. Integration is the first step to achieving these outcomes.

How Genpact built an enterprise-ready MCP server for Kinaxis

Kinaxis RapidResponse is the system of record for demand forecasting, inventory management, and Sales and Operations Planning (S&OP) at Fortune 500 manufacturers. Despite its capabilities, connecting AI agents to RapidResponse required months of custom API development, blocking AI adoption at scale.

Working as an AWS AI Services Competency and Amazon Bedrock AgentCore launch partner, Genpact built the Kinaxis MCP Server—a production-ready integration layer that runs on AgentCore Runtime. It implements the MCP standard with OAuth 2.0 authentication, creating a single secure interface that any AI agent can use to access Kinaxis RapidResponse, collapsing timelines from months to hours.

Why Amazon Bedrock AgentCore

Genpact selected Amazon Bedrock AgentCore as the foundation because supply chain operations demand enterprise-grade reliability. AgentCore delivers secure, serverless hosting with complete session isolation—every interaction runs in its own dedicated microVM.

Without this isolation, a single compromised agent session could expose data across your entire supply chain. AgentCore helps ensure that a procurement agent accessing supplier data is completely isolated from a demand planning agent querying forecast models, even when both run simultaneously.

Figure 1 – Diagram of a supply chain AI integration showing secure agent connectivity. Users connect to MCP Server, which routes requests to Kinaxis RapidResponse using AgentCore.

Flexible deployment and seamless authentication

Organizations can deploy the Kinaxis MCP Server through AWS Marketplace using pre-built Docker images or can customize their deployment using Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS) for specific compliance requirements. Amazon Bedrock AgentCore Identity integrates with Amazon Cognito, Okta, and Microsoft Entra ID. Planners authenticate once and AI agents access Kinaxis securely on their behalf, eliminating credential exposure.

Core capabilities

The Kinaxis MCP Server includes three components built to assist supply chain planning teams:

Workbook data access: Retrieve demand forecasts, inventory positions, and production schedules directly from Kinaxis workbooks
Targeted worksheet extraction: Access specific planning parameters without processing entire workbooks
Advanced parameter customization: Query across site groups, time periods, and multi-dimensional planning hierarchies

All components integrate with Amazon CloudWatch for real-time visibility into agent activities and system performance.

Results

The manufacturer mentioned at the start of this post achieved an estimated reduction in AI agent deployment from 12 weeks to 3 days—a 96% reduction. A global retailer saw up to an 18% improvement in forecast accuracy, reducing stockout rates by an estimated 23% network wide. A pharmaceutical company found that OAuth 2.0 authentication and session isolation provided the security posture needed for internal approval, unblocking AI initiatives that had stalled for months.

Organizations are also discovering a compounding benefit: teams that start with demand forecasting agents expand to inventory optimization and scenario planning—all on the same integration layer. Each new use case can be deployed in days, not months, because the integration work is already done.

Getting started

If your organization runs Kinaxis RapidResponse and AWS today, you’re closer to production-grade supply chain AI than you think. The Kinaxis MCP Server is designed to significantly reduce the integration work that typically takes 3 to 6 months of engineering to days. Organizations have been able to go from zero to deployed AI agents in days, not quarters, while competitors are still scoping their integration projects.

The Kinaxis MCP Server is available now on AWS Marketplace. For a customized deployment, Genpact offers 4-week assessments to design architectures matching your specific security and operational requirements. Contact your AWS account team or Genpact representative to get started.

Genpact – AWS Partner Spotlight

Genpact (NYSE: G) is a global professional services firm with more than 125,000 people across over 30 countries. As an AWS Generative AI Competency Partner and AgentCore launch partner, Genpact stands at the forefront of enterprise AI transformation in supply chain, finance, and operations.

Contact Genpact | Partner Overview | AWS Marketplace

Say Hello to 158 New AWS Competency Partners Added in April

Nick Paris — Wed, 06 May 2026 17:38:26 +0000

By Nick Paris, APN Marketing Manager – AWS Partner Network

The AWS Partner Network (APN) is a global community that leverages Amazon Web Services (AWS) technologies, programs, expertise, and tools to build solutions and provide services for customers.

The APN has more than 130,000 partners from over 200 countries, with 70% headquartered outside of the United States. Together, partners and AWS provide innovative solutions, solve technical challenges, win deals, and deliver greater customer value.

To achieve APN specializations such as AWS Competency, AWS Service Delivery, AWS Service Ready, and AWS Managed Service Provider (MSP), organizations must undergo rigorous technical validation and assessment of their AWS solutions and practices.

NEW! AWS Competency Partners

To succeed with cloud adoption in today’s complex IT environment and continue to advance in the future, customers can team up with an AWS Competency Partner.

The AWS Competency Program validates and promotes AWS Partners with demonstrated technical expertise and proven customer success in specialized areas across industries, use cases, and workloads. Guidance from these skilled professionals can lead to better business and bigger results.

Team up with our newest AWS Competency Partners:

AWS AI Competency

a2c Cloud | NAMER | Agentic AI Consulting Services
A3Data | LATAM | Agentic AI Consulting Services
Actwill | China | Generative AI Consulting Services
Apex Lab | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
Applogika | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Awiant AB | EMEA | Agentic AI Consulting Services
axcess.io | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Axiom Digital | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
binbash | NAMER | Generative AI Consulting Services
Cirrusgo | EMEA | Agentic AI Consulting Services
CloudifyOps | APAC | Agentic AI Consulting Services; Generative AI Consulting Services
CloudNation | EMEA | Agentic AI Consulting Services
CloudPlexo | EMEA | Agentic AI Consulting Services
CloudThinker | APAC | Agentic AI Consulting Services
CLOUDWICK TECHNOLOGIES | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
CloudZenia | APAC | Generative AI Consulting Services
Codex | APAC | Agentic AI Consulting Services
Criptonube – Myappsoftware | NAMER | Generative AI Consulting Services
Cybage | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
develeap | EMEA | Generative AI Consulting Services
DNX Solutions | APAC | Generative AI Consulting Services
DreamSquad Tecnologia | LATAM | Generative AI Consulting Services
E&M Computing | EMEA | Generative AI Consulting Services
Elad Software Systems | EMEA | Agentic AI Consulting Services
ETECH SYSTEM | APAC | Generative AI Consulting Services
gdna | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Globant | LATAM | Generative AI Consulting Services
HiveTek Corporation | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Ibexlabs / Labra | NAMER | Generative AI Consulting Services
INADEV Corporation | NAMER | Generative AI Consulting Services
Insbuilt | LATAM | Agentic AI Consulting Services
IT Visionary | EMEA | Generative AI Consulting Services
Marlocks Technologies | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
Metal Toad | NAMER | Agentic AI Consulting Services
MethodData | NAMER | Agentic AI Consulting Services
Nerdshub | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
Nubiral | LATAM | Agentic AI Consulting Services
Opsfleet | EMEA | Generative AI Consulting Services
Opsfleet | EMEA | Agentic AI Consulting Services
Pentagon System and Services | APAC | Agentic AI Consulting Services; Generative AI Consulting Services
ProfiSea | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
Qucoon | EMEA | Agentic AI Consulting Services
revolgy | EMEA | Agentic AI Consulting Services
Structurit Consulting | EMEA | Agentic AI Consulting Services
superluminar | EMEA | Generative AI Consulting Services
Switch Software | NAMER | Generative AI Consulting Services
Teleglobal International | APAC | Agentic AI Consulting Services; Generative AI Consulting Services
Thaumaturgix, Inc. (Tgix) | NAMER | Generative AI Consulting Services
YASH Technologies | NAMER | Agentic AI Consulting Services
上海邑炎信息科技有限公司 | China | Generative AI Applications
北京尽微致广信息技术有限公司 | China | Generative AI Applications
Agora | | Generative AI Applications
Backlight | NAMER | Generative AI Applications
biia Group | LATAM | Agentic AI Applications; Generative AI Applications
Dovetail | APAC | Generative AI Applications
Exaforce | NAMER | Agentic AI Applications; Generative AI Applications
Grafana Labs | NAMER | Foundation Models and App Development
Kong | NAMER | Infrastructure and Data
monday.com | NAMER | Generative AI Applications
Ona | EMEA | Generative AI Applications
Pixel ML | NAMER | Agentic AI Applications
Plerion | APAC | Agentic AI Tools; Generative AI Applications
Proximie | EMEA | Foundation Models and App Development; Generative AI Applications
Surecomp Development | EMEA | Generative AI Applications
Temporal Technologies | NAMER | Agentic AI Tools
ThinkAnalytics | EMEA | Agentic AI Applications; Generative AI Applications
vFunction | NAMER | Agentic AI Applications

AWS Cloud Operations Competency

DBiz Solutions | APAC | Cloud Governance
EETECH | EMEA | Cloud Governance
Engineering Ingegneria Informatica | EMEA | Operations Management
Epitechnic | EMEA | Cloud Governance
Epsilon | EMEA | Operations Management
Marlocks Technologies | EMEA | Cloud Governance
Mist Avinya Technologies | APAC | Cloud Financial Management
Morris & Opazo | LATAM | Cloud Governance
Several Clouds | EMEA | Cloud Financial Management
UsefulBI Corporation | NAMER | Cloud Financial Management
ZainTech Solution | EMEA | Cloud Governance
Cloudgov | NAMER | Cloud Financial Management; Cloud Governance; Monitoring & Observability; Operations Management

Consumer Goods Competency

Bynder | NAMER | Marketing
Claroty | NAMER | IT & Digital Transformation

AWS Data & Analytics Competency

DataArt | NAMER | Consulting Services
DBiz Solutions | APAC | Consulting Services
Intuitive.Ai | NAMER | Consulting Services
TrackIt | NAMER | Consulting Services

AWS DevOps Competency

10Pearls | NAMER | Consulting Services
Applogika | NAMER | Consulting Services
Appvia | EMEA | Consulting Services
Compass UOL | NAMER | Consulting Services
GoCloud | EMEA | Consulting Services

AWS DevOps Competency

OpsTeam | LATAM | Consulting Services

AWS Digital Sovereignty Competency

Cirrusgo | EMEA | Consulting Services
CloudSoftway | EMEA | Consulting Services
tecRacer | EMEA | Consulting Services
Woodmark Consulting | EMEA | Consulting Services

AWS Education Competency

i2k2 Networks | APAC | Consulting Services

Energy & Utilities Competency

Hypercube Consulting | EMEA | Consulting Services
ZetaMinusOne | LATAM | Consulting Services

AWS Financial Competency

TTEC Digital | NAMER | Banking; General Financial Services; Insurance
Constantinople Operating Company | APAC | Banking
Group-IB Global | EMEA | Banking
Honeycomb | NAMER | Payments
MUREX | EMEA | Capital Markets
Prove | NAMER | Banking; Capital Markets; General Financial Services; Insurance; Payments
Guidehouse | NAMER | Citizen Services
Coder Technologies | NAMER | Defense & National Security

AWS Healthcare Competency

ZiMetrics | APAC | Consulting Services
Avaamo | NAMER | Health Administration

AWS Life Sciences Competency

NDS Corporation | APAC | Consulting Services

AWS Mainframe Modernization Competency

GFT Technologies SE | NAMER | Mainframe Workload

AWS Manufacturing and Industrial Competency

FPT Software | APAC | Enterprise Solutions; Operational Technology; Smart Manufacturing; Smart Products and Services; Supply Chain Management
Mactores Cognition | NAMER | Engineering and Design; Enterprise Solutions; Operational Technology; Operational Technology Security; Smart Manufacturing; Smart Products and Services; Supply Chain Management; Sustainability

AWS Media & Entertainment Competency

EngineLab | EMEA | Content Production
FINCONS GROUP AG | EMEA | Direct to Consumer; Media Supply Chain and Archive
WORKMATES CORE2CLOUD SOLUTION | APAC | Direct to Consumer

AWS Migration and Modernization Competency

Coder Technologies | NAMER | Infrastructure Automation
Alithya | NAMER | Migration Services
CBTS Technology Solutions | NAMER | Migration Services
fourTheorem | EMEA | Migration Services

AWS Migration and Modernization Competency

Sublimation Health | NAMER | Migration Services
Synechron | NAMER | Migration Services
Xpertech Solutions | NAMER | Migration Services

AWS Networking Competency

Advanced Wireless Network (AWN) | APAC | Consulting Services

AWS Nonprofit Competency

BeCloud | NAMER | Consulting Services

AWS Resilience Competency

Welastic | EMEA | Resilience Design

AWS Security Competency

Bytes | EMEA | Identity and Access Management; Infrastructure Protection; Threat Detection and Response
CloudSoftway | EMEA | Infrastructure Protection
Rapyder Cloud Solution | APAC | AI Security
autobotAI | NAMER | Threat Detection and Response
Cato Networks | EMEA | Infrastructure Protection; Threat Detection and Response
Exaforce | NAMER | Identity and Access Management; Threat Detection and Response
Wallarm | NAMER | Application Security

AWS Small and Medium Business (SMB) Competency

Arthurite Integrated Solutions | EMEA | Small and Medium Business
Chengdu Yiding Cloud Technology Co. | China | Small and Medium Business
Devops TechLab | APAC | Small and Medium Business
Elevata | NAMER | Small and Medium Business
Jump Cube | NAMER | Small and Medium Business
SkyGRID Global Technologies | EMEA | Small and Medium Business
Valere | NAMER | Small and Medium Business
Vcloudmaster | EMEA | Small and Medium Business
Cognetiks Consulting | EMEA | Small and Medium Business
Krikey AI | NAMER | Small and Medium Business
Zendesk | NAMER | Small and Medium Business

AWS Migration and Modernization Competency

Xpertech Solutions | NAMER | Migration Services

AWS Networking Competency

Advanced Wireless Network (AWN) | APAC | Consulting Services

AWS Nonprofit Competency

BeCloud | NAMER | Consulting Services

AWS Resilience Competency

Welastic | EMEA | Resilience Design

AWS Security Competency

Bytes | EMEA | Identity and Access Management; Infrastructure Protection; Threat Detection and Response
CloudSoftway | EMEA | Infrastructure Protection
Rapyder Cloud Solution | APAC | AI Security
autobotAI | NAMER | Threat Detection and Response
Cato Networks | EMEA | Infrastructure Protection; Threat Detection and Response
Exaforce | NAMER | Identity and Access Management; Threat Detection and Response
Wallarm | NAMER | Application Security

AWS Small and Medium Business (SMB) Competency

Arthurite Integrated Solutions | EMEA | Small and Medium Business
Chengdu Yiding Cloud Technology Co. | China | Small and Medium Business
Devops TechLab | APAC | Small and Medium Business
Elevata | NAMER | Small and Medium Business

More Value, Greater Profitability for AWS Partners

Our mission is to make the AWS Partner Network (APN) with AWS Marketplace your preferred route to market and empower partners to deliver differentiated value to millions of AWS customers, helping you to win more, win bigger, and win faster.

We’re aligning the AWS Partner experience—including programs, incentives, and enablement—around AWS services and the partner business models: business outcome solutions, technology solutions, managed services, services, and resell.

These enhancements provide you with more relevant and prescriptive guidance, where you need it, and in a consistent and predictable manner. Your profitability, along with helping you drive new business, is our number one goal.

In 2026, we’ll continue to ensure that AWS Marketplace and our AWS Partner programs provide demonstrated pathways to success—helping you drive greater customer value and profitability. There’s an exciting journey ahead, and we’re thrilled to be on it together!

Learn how AWS is transforming the partner experience >>

Simplify multi-account log ingestion to Splunk

Jan Klotter — Mon, 04 May 2026 15:11:48 +0000

By: Jan Klotter, Solutions Architect – AWS
By: Ranjit Kalidasan, Principal Solutions Architect – AWS
By: Paul Frederiksen, Global AWS Partner Technical Leader – Splunk

Splunk

Managing logs across multiple AWS accounts and forwarding them to Splunk has traditionally required high-maintenance, custom-built aggregation pipelines. With Amazon CloudWatch Logs centralization, you can consolidate log data from across your organization into a single account and then forward it to Splunk through one integration point.

This post describes how CloudWatch Logs centralization works, how to architect your Splunk integration around it, and what cost and operational benefits you can expect.

How CloudWatch Logs centralization works

CloudWatch Logs centralization replicates log data from multiple accounts and AWS Regions into a central account. It integrates with AWS Organizations, so you can define rules that automatically scope centralization to your entire organization, specific organizational units, or individual accounts.

Key capabilities include:

Selective log group copying based on your requirements
Automatic merging of same-named log groups in the destination account
Source context enrichment through @aws.account and @aws.region system fields
Optional backup Region setup for resilience
Encryption control for log groups copied from source accounts, including data originally encrypted with customer-managed AWS Key Management Server (AWS KMS) keys

The CloudWatch Log centralization feature is available in 17 AWS Regions at the time this post was published.

What this means for your Splunk integration

The following section describes the architectural design pattern, which will benefit from the architecture pattern with CloudWatch Logs centralization.

Simplify custom solutions

Previously, organizations had to build complex custom-built aggregation solutions to consolidate logs from multiple accounts before forwarding them to Splunk. Previous built solutions can be found here. For example, the traditional approach required creating CloudWatch Logs destinations and AWS Identity and Access Management (IAM) roles in the central account, configuring access policies to allow sender accounts to write log data, and setting up separate subscription filters in each source account—all forwarding through multiple Amazon Data Firehose streams or Amazon Kinesis Data Streams. CloudWatch Logs centralization helps by removing the need for per-account pipeline setup by using centralization rules that automatically replicate log data from source accounts into the destination account, including automatic onboarding of new accounts and log groups based on your organizational scope.

CloudWatch Logs can be forwarded to other destinations using CloudWatch subscription filters, enabling integration with third-party tools like Splunk. The centralization feature works in conjunction with subscription filters to provide a near real-time feed of log events that can be delivered to services such as Kinesis Data Streams, Data Firehose streams, or AWS Lambda for custom processing and forwarding to Splunk.

Recommended architecture for Splunk

The recommended architecture for Splunk includes consolidating the CloudWatch logs by Splunk sources. For example, if you plan to consolidate Amazon Virtual Private Cloud (Amazon VPC) Flow Logs and AWS WAF logs across multiple accounts, consolidate VPC Flow Logs into one log group and AWS WAF logs into another log group in the destination account. This grouping of logs of same data types helps to stream the logs with a Data Firehose subscription filter with the CloudWatch Logs decompression feature along with message extraction, without requiring any additional data transformation using Lambda.

Figure 1: Example architecture for Splunk ingestion

TCO considerations

CloudWatch Logs centralization offers a straightforward pricing model:

Primary destination Region: No additional charge for centralizing logs from source accounts. Standard CloudWatch Logs ingestion and storage pricing applies in the destination account.
Optional backup Region: $0.05 per GB for replicating centralized logs to a second Region for resilience.

The AWS costs of the centralized approach are comparable to a per-account forwarding pipeline. In both cases, you pay for CloudWatch Logs ingestion in the source accounts and Data Firehose delivery to Splunk. Each subscription filter and IAM role requires configuration, monitoring, and error handling. At scale, the engineering time spent maintaining per-account pipelines, troubleshooting failed deliveries, and onboarding new accounts can result in a higher TCO. Centralization helps reduce this to a single pipeline to manage.

Implementation considerations

While the described architecture reduces the operational heavy lifting, the following guidelines should be still followed in a multi-account architecture.

Security and compliance

Centralized logging makes log data readily accessible to investigation teams by aggregating and standardizing logs in one location. You maintain complete control over encryption behavior for copied log groups, and you should verify that centralized data is accessible only to authorized personnel with appropriate access controls in place. When configuring IAM roles and policies for the centralization pipeline and Splunk integration, apply the principle of least privilege by granting only the minimum permissions required for each component to function. For example, the IAM role used by Data Firehose should have permissions limited to reading from specific CloudWatch Logs groups and writing to the designated Splunk endpoint, without broader access to other AWS resources. To detect unauthorized changes to IAM policies or roles used in your centralization pipeline (this solution is described here), you can configure Amazon EventBridge rules that match IAM API calls recorded by AWS CloudTrail and send notifications through Amazon Simple Notification Service (Amazon SNS).

Hybrid approach considerations

Consider whether a purely centralized approach or a hybrid model fits your organization. Application logs and metrics are often best kept in the workload account for day-to-day troubleshooting, while a separate logging account aggregates all workload logs for centralized analysis and operations.

Scaling with your organization

The centralization feature is designed to automatically handle new accounts and log groups without manual configuration updates. If you use AWS Control Tower or other multi-account management solutions, new accounts are automatically included in centralization rules based on your organizational scope. Enable CloudWatch Logs deletion protection on centralized log groups to prevent accidental deletion, and consider using AWS Organizations service control policies (SCPs) to enforce broader logging requirements across member accounts. Define log retention policies in the destination account based on your compliance requirements, and use CloudWatch Logs centralization rule health metrics to monitor replication status across accounts. For organizations with multiple business units, you can create separate centralization rules with different scopes and destination log group naming patterns to maintain logical separation of log data.

Conclusion

You can use Amazon CloudWatch Logs centralization to consolidate logs from across your organization in AWS into a single ingestion point for Splunk. It reduces the operational overhead of custom aggregation architectures while maintaining security boundaries and data lineage.

Splunk – AWS Partner Spotlight

Splunk is an AWS Specialization Partner with Competencies in Cloud Operations, Data and Analytics, DevOps, and more. Leading organizations use Splunk’s unified security and observability platform to keep their digital systems secure and reliable.

Contact Splunk | Partner Overview | AWS Marketplace

Architecting agentic AI for scale and trust from the start

Jerry Chen — Mon, 04 May 2026 15:09:34 +0000

By: Noel Williams, Banking and Capital Markets Leader — PwC Australia
By: Sanya Mediratta, Senior Manager, Digital Trust – PwC Australia
By: Jerry Chen, Senior Solutions Architect – AWS

The race to deploy AI agents is accelerating—and so is the cost of getting it wrong.

Recent research from PwC shows companies with a mature responsible AI program recover faster from incidents. Those without one take more than three times as long—and might never fully recover value.

AI introduces new risks. But many existing controls, such as data governance, access controls, and operational processes, remain essential. In fact, AI can amplify any weaknesses in those controls at speed and scale.

Responsible AI works when organizations integrate AI-specific safeguards into existing risk frameworks and controls are engineered into the agentic platform infrastructure. Controls must be designed upfront that continue to run as core functionality of the agent, throughout the agent’s lifecycle. This is what moves organizations from pilot to scaled value.

You don’t need to start from scratch. But you do need to design trust from day one. This post brings together PwC’s risk and governance experience with Amazon Web Services (AWS) technical architecture and tooling. It sets out a practical blueprint for deploying AI agents safely—and at scale.

Three questions your board will ask

Let’s examine a common use case where your organization deploys an AI agent to handle customer support for triaging inquiries, resolving issues, and escalating complex cases. Within months, it’s successfully handling thousands of interactions daily at impressive speed, cost, and scale.

Before that happens, three questions will determine whether the solution scales and whether it remains trusted:

Can we explain how this solution makes decisions before we deploy it?
Who owns, monitors, and evaluates the overall solution performance, and when should they intervene?
Can we demonstrate sustained and trusted performance, and do we have evidence to prove it?

The following graphic illustrates these questions and how one naturally leads to the next.

Figure 1 – Illustration of the question sequence

The following sections go into each of these questions in detail.

Can we explain how this solution makes decisions before we deploy it?

Your customer support agent will triage thousands of inquiries daily. Before it touches a single customer interaction, your board needs confidence that they understand why it makes those decisions. Explainability isn’t an afterthought—it needs to be architected in from day one.

Operationalizing agentic AI on AWS in the AWS Prescriptive Guidance documentation provides practical frameworks across three dimensions:

Strategy — AI roadmap development with clear linkage to business outcomes, establishing governance frameworks that prioritize explainability from the outset
People — Structured skills development so relevant teams can interpret and communicate AI decisions
Technology — Agentic workflow design and system integration patterns that build observability and explainability into the architecture

AWS also has services and features that are particularly useful for you in this context:

Amazon Bedrock Guardrails Automated Reasoning checks can detect hallucinations, suggest corrections, and highlight unstated assumptions in the response of your generative AI application. More importantly, Automated Reasoning checks can explain why a statement is accurate using mathematically verifiable, deterministic formal logic.
Amazon SageMaker Clarify helps organizations detect bias in their AI models and explain why a model made a specific decision in plain terms that regulators, auditors, and boards can understand.
Amazon Bedrock AgentCore Observability helps you trace, debug, and monitor agent performance in production environments. It offers detailed visualizations of each step in the agent workflow, so you can inspect an agent’s execution path, audit intermediate outputs, and debug performance bottlenecks and failures.

Illustrative PwC governance checkpoints

Ask the following questions before deployment:

Has the board aligned on commercial rationale, values, and risk appetite?
Does this AI use case comply with applicable legal and regulatory requirements?
How does this agent use personal, third-party, and confidential information—in both training data and at inference?
Does the training data reflect all customer segments? Have potential blind spots been identified?
Can individual decisions be explained—not only model performance?
Do outputs make intuitive sense to domain experts?

Who owns, monitors, and evaluates this solution’s performance, and when should they intervene?

Your agent is live. It’s handling 5,000 customer inquiries daily. Performance looks strong initially, but in week three, escalation volumes rise and resolution rates drop.

Without end-to-end observability across the agent’s reasoning chain, your operations team sees only the surface symptoms and can’t isolate the root cause.

Without clear technical thresholds and defined governance ownership, teams start pointing fingers. Nobody knows whether to pause the agent, restrict it to lower-risk interactions, or keep watching.

The AWS Well-Architected Generative AI Lens outlines design principles that help organizations track, control, and intervene in AI agent performance:

Observability and reporting — Comprehensive monitoring of performance, cost, and security across foundation models (FMs)—giving teams a single view of system health
Overload mitigation — Rate limits, workflow tracing, and circuit-breaker patterns to understand how the system behaves under stress and prevent cascading failures
Operational controls — Timeout mechanisms on agentic workflows and automated conditions to halt long-running or anomalous processes before they cause harm

PwC teams have helped customers adopt AWS generative AI solutions to optimize application performance. For example, they used the Well-Architected IaC Analyzer to provide prescriptive recommendations by automatically analyzing infrastructure as code (IaC) artifacts, shortening performance evaluation time from hours to minutes.

Illustrative PwC governance checkpoints

Operational accountability requires clear answers to the following questions:

Is there a designated product owner with well-defined roles and responsibilities across enabling teams?
Have evaluation metrics been defined before deployment—not after the first incident?
Are change and release management controls applied consistently to all AI models?
Does the organization have observability across the agent’s full reasoning chain—not only surface-level performance metrics?
Is there a documented change management process capturing what changed, why, and what was retested?

Can we demonstrate sustained and trusted performance, and do we have evidence to prove it?

The agent has been live for 12 months. A customer disputes a refund denial and lodges a complaint with a financial conduct regulator. The regulator asks, “Show us how you handled this customer’s inquiry. Was the decision fair? How do you know?”

You have 48 hours to respond.

Observability and auditability need to be built into the architecture from the start—not retrofitted under pressure. Three AWS capabilities are foundational here:

Amazon Bedrock AgentCore Observability — AgentCore Observability enables real-time monitoring of agentic systems through Amazon CloudWatch powered dashboards tracking latency, token usage, and error rates, with OpenTelemetry-compatible trace visualizations for debugging performance bottlenecks.
AWS Audit Manager AI best practice framework v2 — AWS Audit Manager provides a prebuilt standard framework to help you gain visibility into how your generative AI implementation on Amazon Bedrock and Amazon SageMaker AI is working compared to AWS recommended best practices.
AWS CloudTrail — Captures API calls for generative AI workloads, including Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases. AWS CloudTrail Lake enables SQL-based querying to reconstruct decision chains.

Illustrative PwC governance checkpoints

Auditability and fairness require ongoing discipline:

Does the organization maintain a complete log of all agent decisions, including the rationale for each?
Are quarterly fairness audits conducted comparing agent performance across customer segments?
How does the organization use continuous monitoring and automated testing to detect model drift?
Is there a documented response protocol for disputes and can the agent’s compliance with that protocol be measured?
Can the organization demonstrate that corrective action has been taken to prevent recurrence?

Building trust at scale

Here’s the competitive reality: organizations that move fastest aren’t the ones that skip governance—they’re the ones that embed it from the start. Transparent decision-making, clear observability and audit trails, and rigorous review processes aren’t constraints on speed. Done right, they’re a competitive advantage, demonstrated to customers through fair outcomes, to boards through measurable controls, and to regulators through auditable evidence.

Australia’s Voluntary AI Safety Standards, published in October 2025, reinforce this approach. The ten guardrails—spanning transparency, human oversight, contestability, and accountability—are principle-based by design, giving organizations latitude to tailor implementation to their specific risk profile. But that flexibility comes with responsibility. In the absence of prescriptive rules, the onus falls on organizations to demonstrate that their governance is fit for purpose.

PwC and AWS bring complementary strengths to this challenge. PwC’s deep experience in governance, risk, and responsible AI frameworks, paired with the technical architecture, tooling, and operational guidance of AWS provides not merely a strategy but a deployable path to AI agents that are trusted and trustworthy.

Ready to get started? Explore the AWS Well-Architected Generative AI Lens and reach out to your AWS and PwC teams to discuss how to safely implement a deployable path to AI agents within your organization.

PwC – AWS Partner Spotlight

PwC is an AWS Premier Tier Services Partner that helps you drive innovation throughout IT and the business to compete in today’s service economy.

Contact PwC | Partner Overview | AWS Marketplace

Orchestrating Intelligent Agents at Scale: How AgentCore and Temporal Create Robust AI Systems

Ruskin Dantra — Mon, 27 Apr 2026 20:42:58 +0000

By: Ruskin Dantra, Sr. Solutions Architect – AWS
By: Shun Mao, Sr. Solutions Architect – AWS
By: Justin Lim, Sr. Solutions Architect – AWS
By: Cornelia Davis, Sr. Staff Developer Advocate – Temporal

Temporal Technologies

Building durable, resilient, distributed systems has always been a challenge. When building traditional distributed systems, you must implement retry logic, circuit breakers, timeout handlers, and complex state machines to handle network failures and service outages gracefully. You often need to write more lines of code to handle these error conditions than for business logic.Agentic AI amplifies these challenges dramatically. Modern AI applications are increasingly running for extended periods of time, coordinating multiple specialized agents, each calling external systems and producing non-deterministic outputs. Where internet-based applications might have historically depended on dozens or hundreds of downstream service invocations, agents serving a user need today regularly involve an order of magnitude more calls over the network. Add human in the loop (HITL) approvals, extended reasoning chains spanning hours or days, and the need to preserve conversation history across failures, and traditional error handling approaches become unmaintainable.

In a previous blog post we covered how Temporal offers durable execution for distributed architectures. In this post, we demonstrate how Temporal integrates with Amazon Bedrock AgentCore to make your agentic workflows durable, resilient, and production ready.

Agentic AI and the durability challenge

Agentic workflows follow a common architecture practice centered around an agentic loop. Each of these agents requires many external calls to different services and systems, and like any distributed system, you need to implement robust error handling and retry mechanisms for these interactions. Your developers must manage dozens of potential failure points when implementing an agent. Moreover, modern AI applications rarely rely on a single agent for a complex business problem. For instance, a financial advisory system might orchestrate a budget analyzer, investment strategist, tax optimizer, and risk assessor. A supply chain application could coordinate demand forecasters, inventory managers, supplier negotiators, and logistics planners. This further increases the likelihood that there will be some failure that needs to be handled during a single customer interaction.

Traditional solutions require developers to write thousands of lines of defensive code: retry logic with exponential backoff, state management to track progress, timeout handlers, circuit breakers, and recovery mechanisms. In this post, we look at how Temporal helps by improving developer velocity and providing production-grade reliability to complex agentic workflow development.

Durable agentic workflows with Temporal

AgentCore is an agentic platform where developers can build, scale and operate AI agents without the need for infrastructure management. Let’s consider the following agentic use case without Temporal where you have created a multi-agent financial advisory system as shown in the following figure:

Figure 1: Multi-agent financial advisory system architecture

In the preceding diagram, each of the agents – the budget, financial analysis and the orchestrator agents – operate in a continuous cycle in which they reason, select tools, execute actions, and respond. Each consists of three core components: the model (reasoning engine), the tools (external capabilities) and the agent (agentic loop). AgentCore provides built-in resilience for agent execution. For complex multi-agent workflows involving multiple coordination points, HITL, approvals, and long-running processes, you can use workflow orchestration tools like Temporal to simplify state management and coordination. Let’s consider the same workload, now with Temporal. Your agentic orchestration logic can be managed by a Temporal workflow. In a multi-agent financial advisory system, the orchestrator agent’s workflow might look like this:

Receive user request: “Help me plan for retirement”
Invoke the model to reason about the request
Delegate to the budget agent (activity: analyze current finances)
Interact with the user to take in additional input
Delegate to the investment agent (activity: evaluate portfolio options)
Delegate to the tax optimizer agent (activity: identify tax-advantaged strategies)
Synthesize the results and return a comprehensive recommendation

Figure 2: Multi-agent financial advisory system architecture with Temporal

The workflow coordinates the agent’s reasoning cycle: invoke the model to analyze the user’s request, determine which tools (or agents) to call, execute those tools (or agents), feed results back to the model for continued reasoning, and ultimately return a response. Temporal orchestrates the workflow—managing retries, pauses, human interactions, and state persistence—while AgentCore makes sure the agents themselves are trustworthy, compliance-aligned, and consistently available. Temporal activities facilitate durability through event sourcing, persisting each workflow step as an immutable event so that completed actions aren’t re-executed. Large language model (LLM) invocations, in addition to the budget and financial analysis agents, are wrapped in Temporal activities. If the LLM is being rate limited, for example, Temporal will manage retries, even taking hints from the model API response to control the retry cycle. As the workflow executes, Temporal tracks progress and automatically preserves results from the LLM and the agent invocations, allowing the workflow to pick up where it left off in the event the process is halted. Resulting in no reissuing of tokens or unexpectedly different outcomes from the LLM in these recovery scenarios. When HITL approval is needed, the combination of Temporal and AgentCore will optimize resource utilization (including freeing compute resources entirely) while waiting as long as necessary for a response from the human. When approval arrives, the workflow state is restored, and the process resumes exactly where it paused. The combination of AI optimized infrastructure management from AgentCore and application management from Temporal means companies can handle millions of complex, high-stakes, long-running workflows with confidence, knowing that failures won’t compromise security, compliance, or user experience.

Customer story

A leading global consumer packaged goods company adopted AgentCore and Temporal to modernize its AI-powered marketing and supply chain workflows. Previously, the company faced challenges with unreliable custom-built state machines and queue-based architectures that struggled to handle millions of transactions and asynchronous interactions. By integrating AgentCore secure runtime with Temporal’s workflow orchestration, the company standardized processes like real-time promotions, loyalty program management, and supply chain automation. This resulted in a 66% reduction in backend costs compared to third-party solutions, improved operational resilience, and the ability to process millions of events monthly with full auditability and observability. The architecture now supports rapid onboarding of new use cases and global scalability while also supporting compliance and governance.

Conclusion

Amazon Bedrock AgentCore powers intelligent agents at scale. Temporal makes it more straightforward to make sure those agents deliver results with enterprise-grade durability. Together, they bridge the gap between AI reasoning and reliable production systems—transforming experimental agents into mission-critical infrastructure. Get started by exploring AgentCore plus Temporal samples. You can also check out Temporal on AWS Marketplace and deepen your understanding through other tutorials and demos.

Temporal – AWS Partner Spotlight

Temporal is an AWS Advanced Technology Partner that offers a robust workflow orchestration solution. The platform delivers increased value to customers through its durable execution framework, which ensures consistency and automatically recovers from system failures.

Contact Temporal | Partner Overview | AWS Marketplace

Announcing the Regional 2026 AWS Partners of the Year for Asia Pacific and Japan

Andrew Somera — Fri, 24 Apr 2026 14:58:01 +0000

By: Andrew Somera, Head of Partner Experiences

We are excited to announce the regional 2026 AWS Partners of the Year for Asia Pacific and Japan (APJ). These annual awards recognize AWS Partner Network (APN) members who consistently deliver exceptional results and drive innovation for our customers.

Regional Awards are comprised of the following four awards:

Consulting Partner of the Year: Recognizes our top AWS Consulting Partners that have provided significant contributions related to revenue, launched opportunities, net new certified individuals, and AWS designations earned.
Rising Star Consulting Partner of the Year: Recognizes AWS Consulting Partners that have seen significant YoY growth in their business.
Technology Partner of the Year: Recognizes our top AWS Technology Partners that are using AWS to lower costs, increase agility, and innovate faster.
Rising Star Technology Partner of the Year: Recognizes AWS Technology Partners that have seen significant YoY growth in their business.

Please join us in congratulating our 2026 AWS Partner of the Year award winners!

India Winners

Consulting Partner of the Year – Tata Consultancy Services (TCS)
Rising Star Consulting Partner of the Year – i2k2 Networks
Technology Partner of the Year – MongoDB
Rising Star Technology Partner of the Year – SentinelOne Inc.

China Winners

Consulting Partner of the Year – Bosicloud
Rising Star Consulting Partner of the Year – Verycloud
Technology Partner of the Year – PingCAP
Rising Star Technology Partner of the Year – OceanBase

Australia & New Zealand Winners

Consulting Partner of the Year – Mantel Group
Rising Star Consulting Partner of the Year – Portable
Technology Partner of the Year – IBM Software and Technology
Rising Star Technology Partner of the Year – Snowflake

ASEAN Winners

Consulting Partner of the Year – Accenture
Rising Star Consulting Partner of the Year – OneByZero Pte Ltd
Technology Partner of the Year – MongoDB
Consulting Partner of the Year: Indonesia – PT Central Data Technology
Consulting Partner of the Year: Malaysia – G-Asiapacific Sdn Bhd
Consulting Partner of the Year: Philippines – Stratpoint Technologies Inc.
Consulting Partner of the Year: Singapore – Accenture
Consulting Partner of the Year: Thailand – DailiTech Co., Ltd.
Consulting Partner of the Year: Vietnam – Renova Cloud Company Limited

Japan Winners

Consulting Partner of the Year – Classmethod
Rising Star Consulting Partner of the Year – Benjamin Inc.
Technology Partner of the Year – Trend Micro Incorporated
Rising Star Technology Partner of the Year – KnowBe4, Inc.

Korea Winners

Consulting Partner of the Year – MegazoneCloud
Rising Star Consulting Partner of the Year – Doosan Corporation Digital Innovation BU
Technology Partner of the Year – Datadog
Rising Star Technology Partner of the Year – Sendbird

Announcing the Regional 2026 AWS Partners of the Year for Europe, Middle East, and Africa

Andrew Somera — Fri, 24 Apr 2026 14:57:10 +0000

By: Andrew Somera, Head of Partner Experiences

We are excited to announce the regional 2026 AWS Partners of the Year for Europe, Middle East, and Africa (EMEA). These annual awards recognize AWS Partner Network (APN) members who consistently deliver exceptional results and drive innovation for our customers.

Regional Awards are comprised of the following four awards:

Consulting Partner of the Year: Recognizes our top AWS Consulting Partners that have provided significant contributions related to revenue, launched opportunities, net new certified individuals, and AWS designations earned.
Rising Star Consulting Partner of the Year: Recognizes AWS Consulting Partners that have seen significant YoY growth in their business.
Technology Partner of the Year: Recognizes our top AWS Technology Partners that are using AWS to lower costs, increase agility, and innovate faster.
Rising Star Technology Partner of the Year: Recognizes AWS Technology Partners that have seen significant YoY growth in their business.

Please join us in congratulating our 2026 AWS Partner of the Year award winners!

Italy Winners

Consulting Partner of the Year – Reply
Rising Star Consulting Partner of the Year – Polarity
Technology Partner of the Year – Salesforce
Rising Star Technology Partner of the Year – Deltatre

Benelux Winners

Consulting Partner of the Year – Cloudar
Rising Star Consulting Partner of the Year – Advascale
Technology Partner of the Year – Snowflake
Rising Star Technology Partner of the Year – Aikido Security

Germany Winners

Consulting Partner of the Year – Capgemini
Rising Star Consulting Partner of the Year – Auvaria
Technology Partner of the Year – Trend Micro Incorporated
Rising Star Technology Partner of the Year – Camunda

Nordics Winners

Consulting Partner of the Year – SoftwareOne
Rising Star Consulting Partner of the Year – APRÓ
Technology Partner of the Year – Snowflake
Rising Star Technology Partner of the Year – Unleash

Central and Eastern Europe Winners

Consulting Partner of the Year – SnapSoft Ltd.
Rising Star Consulting Partner of the Year – GOStack
Technology Partner of the Year – Databricks
Rising Star Technology Partner of the Year – SonarSource SA

United Kingdom and Ireland Winners

Consulting Partner of the Year – Devoteam
Rising Star Consulting Partner of the Year – PricewaterhouseCoopers (PwC)
Technology Partner of the Year – Snowflake
Rising Star Technology Partner of the Year – Redgate Software

France Winners

Consulting Partner of the Year – Capgemini
Rising Star Consulting Partner of the Year – KIIRO
Technology Partner of the Year – Salesforce
Rising Star Technology Partner of the Year – Mistral AI

How ClickHouse powers real-time AI Agents on Amazon Quick

Stanley Chukwuemeke — Thu, 23 Apr 2026 15:21:56 +0000

By: Stanley Chukwuemeke, Senior Partner Solutions Architect – AWS
By: Nakul Mishra, Senior Solutions Architect – AWS
By: Luke Gannon, Principal Product Manager – ClickHouse

ClickHouse

Building real-time AI agents requires instant access to analytical data at conversational speed, and integrating ClickHouse with Amazon Quick can deliver the sub-second query performance needed for conversational analytics to customers. When you build AI agents for customer behavior analysis, anomaly detection, or recommendation systems, database queries that take minutes instead of milliseconds degrade the user experience customers expect.

By integrating ClickHouse with Amazon Quick through Model Context Protocol (MCP), you can build AI agents that deliver sub-second query responses across billions of data points, maintaining natural dialogue flow while providing actionable insights instantly. ClickHouse’s millisecond query performance at scale, combined with Amazon Quick enterprise AI capabilities, gives you the tools to build conversational AI agents quickly and improve your interactive experience.

This post walks you through integrating ClickHouse Cloud with Amazon Quick for conversational analytics. We’ll cover the integration benefits, Amazon Quick MCP server architecture, setup steps, authentication configuration, and AI agent creation for real-time data queries.

Solution overview

Amazon Quick comes with a built-in MCP client that you activate through an integration. After it’s configured, it connects to a remote MCP server, automatically discovers the tools and data sources that server exposes, and makes them immediately available to your AI agents and automations with no custom middleware required. This means you can extend Amazon Quick with action execution, real-time data access, and knowledge base creation through a single integration point.

The following figure shows how customers use Amazon Quick to invoke application capabilities, exposed as MCP tools by independent software vendors (ISVs) such as ClickHouse, enterprise systems, or custom solutions through an MCP integration.

Figure 1: Amazon Quick MCP integration with an external MCP server that exposes application capabilities as MCP tools

Amazon Quick and ClickHouse integration architecture

This integration uses ClickHouse remote MCP server, which acts as the bridge between Amazon Quick AI agent and your ClickHouse Cloud service. Amazon Quick supports integrations with various third-party applications and services, with each integration supporting different combinations of actions and knowledge base creation capabilities. The following figure shows a reference architecture for integrating ClickHouse Cloud on AWS to Amazon Quick using ClickHouse remote MCP server. For more information, refer to Amazon Quick supported integrations.

Figure 2: Amazon Quick and ClickHouse integration through ClickHouse remote MCP server

Integration benefits

When AI agents need to make real-time decisions, waiting minutes for database queries fundamentally breaks the conversational experience and limits agent autonomy. ClickHouse columnar storage architecture delivers query results in milliseconds across billions of rows, ensuring AI agents rarely wait on data. With built-in support for streaming inserts and integrations with Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon Kinesis, and other event pipelines, ClickHouse ensures that the data your agents reason over is always current. High-volume event and telemetry data that AI agents depend on can quickly become very expensive to store. ClickHouse’s ability to compress data on disk reduces the cost of storing information and the amount of resources needed to query, boosting query performance. ClickHouse speaks standard ANSI SQL with extensions to the language, making it straightforward to connect to existing business intelligence (BI) tools and orchestration layers without specialized adapters. ClickHouse Cloud separates compute from storage, helping organizations to scale query capacity on demand during peak AI workloads without over-provisioning and incurring idle infrastructure costs.

The combination of ClickHouse Cloud and Amazon Quick solves three fundamental business challenges: moving from insight to action in real time, giving nontechnical teams access to complex datasets, and the ability to scale volume without increasing cost.

Amazon Quick AI agents can now query live operational data and act on it. Agents can provide recommendations and trigger workflows or flag anomalies the moment they emerge, rather than after a reporting cycle has completed.

Using Amazon Quick, business users can query complex datasets without writing SQL. Paired with ClickHouse, those natural language queries now run against a database engineered for speed at scale, providing nontechnical teams with accurate, real-time answers without being blocked by data engineering backlogs.

As data volumes grow, most integrations degrade, producing slower queries, higher costs, and more infrastructure to manage. This integration scales with your business. ClickHouse Cloud handles large-scale data while Amazon Quick agents remain responsive, keeping the experience consistent for you regardless of data volume.

Integrating ClickHouse with Amazon Quick

Before you start this integration, check with your security and compliance team to understand your organization’s requirements when integrating tools.

To set up the integration, connect to your ClickHouse Cloud service to retrieve the ClickHouse remote MCP server endpoint.

Currently, the Amazon Quick client MCP can only connect to remote MCP servers that are accessible publicly. Refer to Security and MCP for ClickHouse guidance on exposing their MCP publicly.

To create an MCP integration in Amazon Quick:

Sign in to the Amazon Quick console with a user that has Author permissions or higher.
From the integration grid, choose Model Context Protocol (MCP).
Choose the plus sign ( + ).
On the Create integration page, enter a Name, Description, and your MCP server endpoint URL. Choose Next, as shown in the following screenshot.
Choose Create and then Continue. Review the discovered tools and data capabilities from your MCP server.
When you’re finished, choose Done.

Figure 3: Amazon Quick MCP Integration page

To authenticate to ClickHouse Cloud using OAuth:

After the integration becomes available, choose Sign in to complete authentication.
Authenticate using your ClickHouse Cloud credentials.
If you want other users to use the integration, share it.

The following screenshot shows the ClickHouse sign-in page.

Figure 4: ClickHouse sign-in

After the integration is complete, you create a custom chat agent and add the connector to create a conversational interface that answers specific business questions. Optionally, you can also add this connector to your Amazon Quick space and share it with your team or other parts of your organization either in isolation or across multiple agents. This way, you can scale the integration across multiple agents or combine it with other data sources in the space. Another option is to use this integration with Amazon Quick Flows to automate repetitive tasks and Amazon Quick Research to get a deep understanding of a specific topic by using data in ClickHouse in combination with other sources.

Powering real-time AI Agent with ClickHouse

Through the Amazon Quick AI agent chat interface, business users can query ClickHouse directly using natural language with no SQL knowledge required. Amazon Quick passes the conversation to the ClickHouse MCP server to translate those conversation inputs into SQL actions executed against the ClickHouse database, returning insights at the speed ClickHouse is known for, regardless of data volumes. The ClickHouse MCP server provides a tool called run_select_query that can be used to run a SQL SELECT statement against a ClickHouse database. The MCP server implements all the logic needed to perform that action when the tool is used, for example, for creating a connection or authentication.

For example, in the Amazon Quick AI chat agent, you can ask a question that requires real-time data, such as “What were the top 5 products by revenue yesterday?” The agent should invoke the ClickHouse MCP connector, discover the relevant schema, execute the SQL query, and return a synthesized natural language response with the results.

The following figures show a sample interaction using Amazon Quick AI chat agent.

Figure 5: Sample interaction using Amazon Quick AI chat agent

Conclusion

The integration of ClickHouse Cloud with Amazon Quick delivers sub-second query responses for AI-powered analytics. By combining ClickHouse analytical performance with Amazon Quick AI agents’ natural language capabilities, organizations can unlock real-time insights that transform how teams interact with their data. Using this integration, you can query complex datasets conversationally and make data-driven decisions faster. Start building your first real-time AI analytics agent today by exploring this integration setup for your own use cases and experience how the combination of ClickHouse speed and Amazon Quick intelligence can accelerate your analytics, workflow, and research journey.

For next steps, review Model Context Protocol (MCP) integration and Integrate external tools with Amazon Quick Agents using Model Context Protocol (MCP) for detailed implementation required by third-party partners to integrate with Amazon Quick using MCP.

To get started, sign up for ClickHouse Cloud and Amazon Quick to build your first real-time AI analytics agent.

ClickHouse – AWS Partner Spotlight

ClickHouse, Inc is an AWS Advanced Technology Partner and AWS Competency Partner that is behind ClickHouse, a fast, open source columnar database built for real-time analytics at scale. Engineered for high performance, ClickHouse Cloud delivers exceptional query speed and concurrency, and it’s purpose-built for AI agents generating frequent, complex queries. Trusted by leading companies like Sony, Tesla, Anthropic, and Lyft, ClickHouse delivers a high-throughput, low-latency platform for instant insights from massive data volumes.

Contact ClickHouse | Partner Overview | AWS Marketplace

How SAS Viya Workbench on AWS accelerated analytics innovation

Doug Mbaya — Wed, 22 Apr 2026 20:55:36 +0000

By: Doug Mbaya, Sr Partner Solutions Architect – AWS
By: Dmitry Frizner, Solutions Architect – AWS
By: Erin McCarthy, Senior Product Marketing Manager – SAS

SAS

Data scientists and developers today need to deliver insights faster while managing growing data volumes and meeting compliance requirements. SAS Viya Workbench on Amazon Web Services (AWS) helps accelerate analytics innovation by providing a cloud-centered environment where your team spends less time managing infrastructure and more time building models. If environment configuration, version conflicts, and security approvals are slowing your analytics projects, this solution offers a path forward.

In this post, we explore how SAS Viya Workbench on AWS addresses these challenges through its split-plane architecture, examine real-world use cases across industries, and show you how to get started.

Meeting developers where they are

Working across multiple languages and frameworks such as Python notebooks, R scripts, SAS procedures, and SQL-based workflows creates real obstacles when these tools run on disconnected local machines or legacy infrastructure.

Every environment needs its own infrastructure, security controls, software versions, and data access. Spending valuable time on managing these elements means neglecting more important work. Data scientists often spend hours installing software, resolving version conflicts, and configuring machines when they could be exploring data and generating insights.

Limited access to tools or environments, incompatible workflows, and slow approval processes create barriers to collaboration. Teams resort to emailing files or manually transferring datasets. The lack of a single, shared workspace leads to version mismatches and broken code.

Maintaining robust security and properly complying with regulations is essential, but the process of doing so can slow teams down. They might have to wait days or weeks for IT to approve new tools and infrastructure. But if they bypass controls, they put the organization at risk.

Many software professionals use standard laptops for work. But with fixed memory and processing power, such tools can cause crashes or force analysts to work with only a small sample of the data when demands spike.

Traditional approaches, including maintaining separate environments for each language, manually provisioning compute resources, or relying on desktop installations, struggle to address these challenges together. They typically solve one problem while creating others, forcing tradeoffs between flexibility, security, and scalability.

How SAS Viya Workbench on AWS addresses these challenges

AWS has partnered with SAS to bring SAS Viya Workbench to AWS Marketplace. This unified analytics environment reduces these tradeoffs by offering flexibility in programming languages, cloud scalability, and data custody.

SAS Viya Workbench offers a choice of languages. Your team can work in Python, R, and SAS within a single cloud-based environment, without having to switch between tools or manage separate installations.

By integrating with Amazon Simple Storage Service (Amazon S3), Amazon EMR, and Amazon Redshift, SAS Viya Workbench provides elastic compute that grows with your workload.

You maintain custody of your data within your AWS account. SAS manages only the control plane for orchestration.

With a few clicks, your team can provision fully managed development environments that are secure, collaborative, and optimized for modern analytics workloads.

Understanding the split-plane architecture

Traditional analytics solutions often force a choice between security and flexibility. SAS Viya Workbench addresses this tradeoff through a split-plane architecture.

Before diving into the benefits, review the functions of the control plane and data plane:

Control plane handles management functions such as user authentication, environment configuration, and orchestration.
Data plane processes your actual workloads, running queries, training models, and storing results.

The separation of the control plan and the data plane delivers specific advantages for your organization. Your AWS account contains all compute resources, data storage, and processing engines. Your data doesn’t leave your environment, so you maintain data custody. SAS handles environment orchestration, user management, and service coordination through the SAS-managed control plane, without accessing your data. Your stakeholders in your organization benefits from SAS analytics expertise, while your security team maintains full visibility and control. Connectivity between planes is encrypted with TLS for secure communication without sacrificing performance.

By using this design, your organization maintains control over data and compute resources while benefiting from SAS’s advanced analytics and AI capabilities.

The solution addresses enterprise security requirements through comprehensive protections. All customer data remains within your AWS account, maintaining data sovereignty. End-to-end encryption protects data in transit and at rest. Granular role-based access controls integrate with AWS Identity and Access Management (IAM) and third-party identity providers. Comprehensive logging and monitoring support compliance requirements, with adherence to industry standards including AWS System and Organization Controls (SOC) 2, General Data Protection Regulation (GDPR), and Health Insurance Portability and Accountability Act (HIPAA).

The following diagram shows the SAS Viya Workbench split-plane architecture.

Figure 1: SAS Viya Workbench architecture diagram

Real-world use cases

Organizations across industries are applying this approach to accelerate their analytics initiatives.

In life sciences, cross-functional research teams analyze genomic data at scale using R and SAS in the same environment. One research organization reduced environment configuration time significantly, which freed scientists to focus on analysis rather than infrastructure setup.

Financial services firms improve fraud detection pipelines by integrating Python-based machine learning (ML) with SAS regulatory models. Risk teams maintain compliance while accelerating model development cycles.

Manufacturing operations teams optimize supply chains through real-time analytics pipelines using Amazon S3 and Amazon EMR. When demand forecasting requires additional compute resources, the environment scales automatically rather than requiring manual intervention.

Marketing analytics enhance customer segmentation and campaign effectiveness through integrated multichannel data analysis and predictive modeling.

Retail analytics teams create personalized shopping experiences and optimize inventory through advanced demand forecasting and customer behavior analytics, combining multiple data sources in unified workflows.

Enhancing developer productivity with Workbench

Now that you understand the architecture, let’s examine how SAS Viya Workbench improves daily workflows.

Built-in Git support means that developers can collaborate with each other, track changes, and maintain quality code without leaving the environment. Prebuilt templates for Python, R, and SAS models and workflows reduce development time from weeks to days because teams start with proven patterns rather than blank screens. The browser-based integrated development environment (IDE) eliminates local setup and configuration, so developers can launch code, test models, and deploy from their preferred browser. Automatic self-termination shuts down idle sessions after a specified period, helping manage computing costs efficiently.

With infrastructure management handled by the environment, your team focuses on building, training, and deploying models, whether using R for statistical modeling, Python for ML, or SAS for regulatory reporting.

The following table summarizes the key benefits:

Benefit	Value delivered
Multi-language support	Flexibility to choose Python, R, or SAS without switching environments
Enterprise security	Data stays in your AWS account
Cloud-based scalability	Auto scaling with AWS services
Faster time to insight	Prebuilt environments and templates
Reduced overhead	No infrastructure to manage

Why this matters for your organization:

Democratized analytics access – SAS Viya Workbench provides an intuitive, web-based interface for technical users and data scientists who prefer a code-first approach. Teams work with preferred programming languages while maintaining consistency in governance and deployment.
Elastic scalability – The environment takes advantage of the elasticity of AWS and automatically scales compute resources based on workload demands, supporting optimal performance during peak usage and minimizing costs during idle periods.
Accelerated time-to-value – Teams can start analyzing data immediately because of the pre-configured environments and extensive example libraries. They can use existing Python, R, or SAS code with minimal modifications, start from prebuilt templates, or code from scratch.
Collaborative innovation – Built-in collaboration features support project teamwork, insight sharing, and model deployment. Version control, project templates, and shared computing resources foster innovation while maintaining reproducibility.

Conclusion

SAS Viya Workbench on AWS combines the analytical power of SAS with the scalability and security of AWS infrastructure. The split-plane architecture addresses data security and sovereignty concerns while providing collaboration and scalability benefits of cloud-based solutions.

For organizations that want to modernize analytics capabilities, this solution offers a compelling path forward that focuses on generating insights rather than managing infrastructure. At the same time, they maintain the necessary enterprise-grade security and compliance.

Get started today

Explore SAS Viya Workbench in AWS Marketplace to see how your team can:

Collaborate securely across Python, R, and SAS
Scale analytics workloads on demand
Speed up model development and deployment

Contact your AWS account team or visit the SAS Viya Workbench listing in AWS Marketplace to learn more.

We’d love to hear about your analytics modernization journey; share your experiences in the comments section.

SAS – AWS Partner Spotlight

SAS is an AWS Advanced Technology Partner and AWS Competency Partner and the founder and future of analytics, harnessing the power of data and AI to shorten the path from questions to answers. The result is solutions like SAS® Viya® Workbench and SAS Customer Intelligence 360 that deliver productivity, performance and trust to help our customers make better decisions, faster

Contact SAS | Partner Overview | AWS Marketplace

Build a secure, scalable deployment pipeline with CircleCI’s AWS Deployment Pipeline Reference Architecture implementation

Shalabh Nigam — Wed, 22 Apr 2026 20:43:04 +0000

By: Shalabh Nigam, DevSecOps Solutions Architect – AWS
By: Nitin Kuriakose, Senior Field Engineer – CircleCIer – CircleCI

CircleCI

Modern software teams face the critical challenge of needing to deploy applications faster while maintaining security and compliance. Organizations in regulated industries spend significant sprint capacity on manual compliance checks. Enterprises managing multi-Region deployments face extended deployment windows with elevated failure risks. Security scanning often becomes a bottleneck rather than an automated safeguard.

CircleCI addressed these challenges by building a reference implementation of the AWS Deployment Pipeline Reference Architecture (DPRA). This implementation demonstrates how to build enterprise-grade deployment pipelines that are secure by design, scalable across Regions, and integrated with best-in-class DevOps tools. This post explores how CircleCI created this implementation and how organizations can use it as part of CircleCI’s Integrated DevOps Toolchain (IDT).

Understanding the challenge

Organizations building deployment pipelines face challenges that standard continuous integration and continuous delivery (CI/CD) tools don’t adequately address. The compliance burden means organizations spend significant sprint capacity on manual security reviews and audit documentation. Multi-Region complexity creates deployment windows extending for hours, with each Region representing a potential failure point. The security scanning gap occurs when organizations struggle to implement comprehensive scanning without creating bottlenecks—requiring integration of multiple tools for container vulnerabilities, secrets detection, static analysis, and infrastructure compliance. The tool integration challenge emerges as enterprises manage 10–20 tools in their DevOps toolchain, with integration maintenance diverting engineering resources from feature development.

These challenges create a false choice between speed and safety. CircleCI’s DPRA implementation eliminates this trade-off.

What is the AWS Deployment Pipeline Reference Architecture?

The DPRA provides prescriptive guidance for building secure, automated deployment pipelines on AWS. It defines architectural patterns for progressive deployments, automated testing, security scanning, and multi-environment orchestration. Rather than prescribing specific tools, the DPRA establishes a framework that organizations can implement using their preferred CI/CD platform and DevOps tools. CircleCI’s reference implementation demonstrates how to realize the DPRA vision using CircleCI’s CI/CD platform integrated with AWS services and third-party security tools. This implementation serves as a reference blueprint that organizations can fork, customize, and deploy in their own environments.

Why CircleCI built a DPRA reference implementation

CircleCI built this reference implementation to address a fundamental question their customers and partners ask: “What does great look like in a DevOps pipeline?” Organizations know they need to modernize their deployment practices, but they lack concrete examples of reference architectures demonstrating best practices.

The reference implementation serves multiple strategic purposes: accelerate customer adoption by providing a working example that customers can fork and customize; demonstrate AWS integration depth through built-in integration with AWS services including AWS CodeDeploy, Amazon CloudWatch, Amazon Elastic Container Service (Amazon ECS), and Amazon Aurora; validate CircleCI’s IDT by proving the approach delivers measurable outcomes; and enable partner replication by creating a blueprint that AWS Partners can follow to build their own DPRA implementations.

About CircleCI

CircleCI is a standalone CI/CD platform with deep AWS integrations and more than 3,500 partner integrations across the DevOps toolchain. Teams standardize on CircleCI to accelerate delivery; adopt platform-agnostic infrastructure as code (IaC) such as AWS Cloud Development Kit (AWS CDK), Terraform, or Pulumi; and enforce security by design with OpenID Connect (OIDC), policy controls, and comprehensive testing insights. And they can achieve all this without being tied to a single cloud or version control system. CircleCI’s AI-powered capabilities include automated flaky test remediation that identifies and resolves intermittent test failures without manual intervention.

CircleCI’s platform provides enterprise-grade features including reusable configuration, CircleCI Contexts for secure credential management, Test Insights for tracking performance trends, Workspaces for artifact transfer, Matrix Jobs for multi-Region deployments, and the Orb ecosystem with pre-built, reusable configuration packages available at Orbs in the CircleCI Developer documentation.

CircleCI and AWS: A strategic partnership

The collaboration between AWS and CircleCI represents a proven approach to DevOps transformation validated across hundreds of customer implementations. CircleCI holds the AWS DevOps Competency, demonstrating deep expertise in AWS services and best practices. Unlike generic CI/CD tools that treat AWS as merely another deployment target, CircleCI’s AWS integration includes built-in OIDC authentication that eliminates long-lived credentials, optimized AWS service integration with CodeDeploy, CloudWatch, Amazon ECS, and Aurora, first-class AWS CDK support with flexibility for Terraform or Pulumi, and a platform-agnostic approach supporting multi-cloud and hybrid environments. This partnership enables the IDT, a comprehensive solution delivering turnkey excellence through pre-validated tool combinations, personalized solutions from more than 3,500 integrations, fast time to value with pre-built configurations, cost optimization with transparent pricing, and compliance-ready security controls.

Solution architecture

CircleCI’s DPRA reference implementation demonstrates enterprise-grade deployment patterns across three AWS environments: beta, gamma, and production. The implementation deploys a Java Spring Boot API running on Amazon ECS with Aurora database, showcasing real-world architectural patterns.

The architecture incorporates progressive deployment with AWS CodeDeploy for blue/green deployments with automated rollback based on CloudWatch alarms; multi-Region orchestration using CircleCI matrix jobs for parallel deployments; comprehensive security scanning with Trivy (container vulnerabilities), GitGuardian (secret detection), SpotBugs (static analysis), and cdk-nag (infrastructure compliance); automated testing with JUnit, SoapUI, and JMeter integrated with CircleCI Test Insights; and IaC using AWS CDK with reusable constructs.

The following diagram illustrates this architecture.

Figure 1: CircleCI DPRA reference implementation architecture

CircleCI made several critical architectural decisions. They chose to use OIDC over long-lived credentials for AWS authentication, eliminating security risks and simplifying credential rotation. They implemented security scanning at every stage rather than as an end-of-pipeline gate, catching issues earlier when they’re cheaper to fix. Platform-agnostic tooling supported AWS CDK, Terraform, and Pulumi to avoid vendor lock-in. Progressive deployment patterns using blue/green deployments with automated rollback minimized risk while maintaining rapid release velocity.

Business impact

Organizations implementing CircleCI’s DPRA reference implementation as part of the IDT typically achieve the following improvements:

50% reduction in deployment time through automated progressive deployments and parallel Region rollouts
85% decrease in security vulnerabilities using integrated scanning
3 times faster incident resolution with comprehensive monitoring using CloudWatch Synthetics and CircleCI Test Insights
40% reduction in configuration errors through reusable CircleCI configurations and pre-commit hooks.

Beyond these metrics, organizations report increased developer confidence in deployments, reduced on-call burden through automated rollbacks, and faster onboarding for new team members using documented reference patterns.

Getting started

Organizations can implement CircleCI’s DPRA reference implementation in an afternoon. Begin by forking the reference implementation from the GitHub repository, connect the repository to your CircleCI account, configure AWS credentials using OIDC authentication, customize the AWS CDK templates for your application and environments, and deploy the infrastructure to run your first pipeline.

The repository includes comprehensive documentation, preconfigured pipeline definitions, and step-by-step setup instructions. For organizations ready to go beyond the reference implementation, CircleCI and AWS offer IDT workshops where solution architects help design complete toolchain strategies. For partners interested in building their own DPRA implementations, the reference implementation serves as a blueprint. The AWS DPRA documentation provides additional guidance.

Building your own IDT and contributing to DPRA

AWS Partners can build their own IDT implementations to extend the DPRA ecosystem with validated, approved reference solutions. To do so, you need to select your core toolchain (CI/CD, security, monitoring, and IaC tools), build the reference implementation by forking DPRA architecture and integrating your tools, validate with pilot customers, and contribute back by publishing to GitHub with documentation.

For technical requirements, you need AWS service integration through OIDC, CodeDeploy, CloudWatch, and Amazon ECS or Amazon Elastic Kubernetes Service (Amazon EKS), multistage security scanning, progressive deployment patterns with automated rollback, and IaC with reusable modules.

To contribute to the DPRA ecosystem, submit implementations through the AWS Partner Network portal with architecture diagrams and guides. Participate in DPRA working groups and present at AWS events. Benefits include marketplace differentiation, accelerated customer adoption, deeper AWS partnership, and the potential to be featured in AWS case studies and documentation.

Conclusion

CircleCI’s implementation of the AWS DPRA demonstrates that organizations don’t have to choose between deployment speed and security. By combining the robust cloud infrastructure of AWS with CircleCI’s flexible CI/CD platform and an ecosystem of more than 3,500 integrations, organizations can build deployment pipelines that are secure by design, scalable across Regions, and tailored to their specific requirements. The reference implementation serves as more than merely a technical blueprint—it’s a gateway to comprehensive DevOps transformation through the IDT. Whether you’re starting with the reference implementation or ready to design your complete IDT, CircleCI and AWS provide the expertise, tools, and support to accelerate your DevOps journey.

To get started as an AWS Partner, review the DPRA documentation and CircleCI’s reference implementation, identify your value proposition, and engage your AWS Partner Development Manager. After accessing AWS Partner Central resources, join the DPRA partner community for support. To get started with CircleCI’s Deployment Pipeline Reference implementation, visit the aws-deployment-pipeline-reference-architecture source code on GitHub. Visit CircleCI in AWS Markteplace.

CircleCI – AWS Partner Spotlight

CircleCI is an AWS DevOps Competency Partner and a leading CI/CD platform with deep AWS integrations and more than 3,500 partner integrations across the DevOps toolchain. CircleCI helps organizations accelerate delivery, adopt platform-agnostic infrastructure as code, and enforce security by design—all without being tied to a single cloud or version control system.

Contact CircleCI | Partner Overview | AWS Marketplace

Impetus LeapLogic’s automated path to Amazon SageMaker

Sujit Singh — Tue, 21 Apr 2026 18:19:34 +0000

Impetus Technologies

By: Sujit Singh, Senior Partner Solutions Architect – AWS
By Dayananda Swamy VT, Partner Solutions Architect – AWS
By Ashish Dahiya, Principal Architect, Impetus Technologies
By Arpan Bhandari, Technical Architect, Impetus Technologies
By Gurvinder Arora, Product Marketing, Impetus Technologies

Legacy on premises machine-learning (ML) environments are often constrained by fixed infrastructure, fragmented tooling, and manual deployment processes. As data volumes grow and models become more complex, these limitations slow experimentation, delay production rollouts, and increase operational overhead.

By migrating these workloads to Amazon SageMaker, enterprises can standardize model development, automate training and deployment, and scale ML workloads on demand without managing underlying infrastructure. SageMaker provides a managed foundation for experimentation, versioning, monitoring, and production grade deployment across the ML lifecycle.

Traditional migration approaches often require months of manual refactoring, custom scripting, and repeated validation cycles, introducing delivery risk and slowing business outcomes. Impetus LeapLogic helps reduce this complexity by automating a significant portion of the migration to SageMaker, minimizing manual effort and accelerating time to production.

LeapLogic, Impetus’s automation led modernization system, helps enterprises transition legacy data and ML workloads into cloud-centric architectures on Amazon Web Services (AWS). Backed by deep experience in data engineering, analytics, and ML modernization, Impetus guides customers through migration from legacy environments to production-ready SageMaker deployments.

Using automated analysis, code and pipeline transformation, and structured validation, LeapLogic enables enterprises to modernize faster while maintaining accuracy, continuity, and operational stability.With Impetus LeapLogic, enterprises can:

Standardize ML development and deployment on SageMaker
Automate large portions of legacy pipeline and workload transformation
Reduce manual refactoring and migration risk
Coexist with existing AWS services while improving ROI on current investments
Move ML workloads to production with greater consistency and governance

This blog post describes how SageMaker and Impetus LeapLogic enable a structured, lower risk modernization of legacy workloads on AWS.

AI operationalization with SageMaker: Scaling ML from development to production

SageMaker streamlines the ML journey by combining automation, scalability, and governance into a single managed environment. From intelligent data prep and model selection to distributed training, automatic tuning, collaborative notebooks, and governed model cataloging, SageMaker helps trim the operational clutter and accelerate teams toward production with fast, monitored, versioned deployments and built-in model explainability.

An integrated experience with SageMaker Unified Studio

SageMaker supports the enterprise data management lifecycle, including data ingestion, scalable ML development, secure model deployment, and advanced analytics, enabling continuous improvement from a single, fully managed interface.Let’s see how these services operate cohesively and offer an integrated environment.

Figure 1: SageMaker Unified Studio with complete AWS stack

The architecture shown in the preceding diagram illustrates how enterprise data flows through a unified SageMaker environment, beginning with diverse real-time and batch sources entering AWS through services such as Amazon Kinesis, Amazon Managed Streaming for Apache Kafka (Amazon MSK), and AWS IoT Core. This raw stream is shaped and refined through AWS Glue, AWS Glue Studio, and AWS Glue DataBrew before landing in the curated zones that support analytics engines such as Amazon Redshift, Amazon EMR, Amazon OpenSearch Service, Amazon Quick Sight, and Amazon Athena. Amazon SageMaker Unified Studio, the integrated development environment within SageMaker, is used to orchestrate feature engineering, experimentation, training, and deployment within a single ML workbench, while also drawing on surrounding AI services for added intelligence. The result is a cohesive environment where data movement, transformation, modeling, and insight generation operate as an integrated workflow, giving teams a governed, scalable, and production ready path from ingestion to applied machine learning.

Real world enterprise use cases

SageMaker excels as a centralized interface and integrated environment for data warehousing, extract, transform, and load (ETL) processes, BI reporting, orchestration, advanced analytics, generative AI, automated machine learning (AutoML), explainability, and model monitoring.By seamlessly integrating with a variety of AWS services, SageMaker unlocks powerful use cases, such as the following, that help drive business value, accelerate AI adoption, and optimize operational efficiency.

Automated data preparation, feature engineering, and predictive modeling
Unified metadata management
Interactive ML-driven BI
Data access controls, usage, monitoring, anomaly detection, and compliance with regulations

Impetus LeapLogic – Elevate intelligence

LeapLogic is Impetus’s automation system designed to modernize complex legacy data, analytics, and ML workloads into cloud native AWS architectures. It supports modernization across data warehouses, ETL systems, orchestration tools, analytics layers, and ML pipelines, enabling enterprises to transition to SageMaker with reduced manual effort and predictable outcomes.Trusted by several Fortune 500 enterprises, LeapLogic simplifies data system modernization with a four-step paradigm:

Workload assessment offers automated workload analysis with actionable recommendations
Business logic transformation translates legacy logic into cloud native vernacular
Validation and reconciliation facilitates seamless business continuity
Operationalization launches your cloud native future.

Beyond LeapLogic, Impetus, as a premier data and AI system integrator, plays a vital role in rapidly developing enterprise class generative AI solutions. With their generative AI Innovation Labs—the industry’s first strategy, design, and build collaborative service offering—enterprises can create a production ready generative AI prototype in less than six weeks.

LeapLogic-powered fully automated stack mapping

Let’s see how LeapLogic with its new AI enabled conversion capabilities, along with Impetus’s transformative services, helps enable automated transformation of legacy tools and technologies to AI-ready SageMaker and its supporting stack or alternative options such as PySpark. The following shows the usage categories and the LeapLogic process to map to AWS services.

Data sources and storage

Automates schema, metadata, and data migration into AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon DynamoDB, and Amazon Relational Database Service (Amazon RDS) by parsing legacy structures and generating equivalent cloud native storage models
Creates AWS Lake Formation blueprints by converting legacy governance rules and access patterns into reusable templates
Generates validated, optimized AWS workloads by profiling legacy pipelines and producing performance tuned equivalents

Workload modernization

Translates ETL and extract, load, and transform (ELT) logic, jobs, and orchestration scripts into assets compatible with AWS Glue, Amazon EMR, or Amazon Redshift using automated semantic mapping of transformations
Reconstructs business logic by interpreting legacy operators, functions, and procedures and generating their AWS service counterparts
Performs end-to-end functional and row level validation using AWS Glue and Amazon EMR jobs to facilitate accuracy and readiness for production
Auto provisions the appropriate AWS compute and orchestration services needed to implement in production

Compute and infrastructure

Maps on-premises execution patterns to cloud compute constructs (such as Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda, AWS Fargate, or AWS Batch) by analyzing concurrency, scheduling, and resource needs
Generates infrastructure as code (IaC) deployment templates that automate provisioning, scaling, and operationalization of migrated workloads

Analytics and visualization

Converts Tableau, Cognos, OBIEE, SAS, and Alterx assets into analytical views backed by Amazon Quick Sight, Athena, and Lake Formation by mapping visual and semantic layers
Recreates dashboards and workflows using AWS datasets, facilitating functional parity and improved performance
Suggests conversational analytics enhancements using Amazon Q where applicable
Provisions Amazon CloudWatch dashboards and alerts to monitor analytical workloads

Security, identity, and compliance

Translates existing access policies into AWS Identity and Access Management (IAM) and Lake Formation roles, permissions, and fine-grained access controls
Modernizes identity management by mapping legacy authentication models to identity flows compatible with IAM or Amazon Cognito
Configures SageMaker built-in encryption, key management (AWS Key Management Service (AWS KMS) or AWS CloudHSM), audit trails, and compliance templates aligned to regulatory standards

AI and ML services

Establishes an enterprise generative AI foundation through Impetus’s Strategy, Design, and Build Labs that map business use cases to working prototypes in weeks
Integrates Amazon Bedrock models by generating prompt flows, orchestration logic, and evaluation frameworks
Automates provisioning of Amazon SageMaker AI components, training pipelines, and inference endpoints needed for scalable AI adoption

Internet of Things (IoT)

Configures AWS IoT Core, AWS IoT Greengrass, and AWS IoT FreeRTOS integrations by mapping legacy sensor flows to real-time ingestion pipelines
Builds edge-to-cloud pathways that help transform streaming IoT data into actionable insights using AWS analytics and ML services
Facilitates SageMaker-ready IoT data pipelines for training, inference, and continuous optimization

Security and compliance

Customers implementing LeapLogic must follow AWS security best practices and the AWS Well-Architected Framework Security Pillar. Deployments should adhere to the principle of least privilege for IAM roles, implement encryption for data at rest and in transit, and use AWS security services such as AWS IAM Access Analyzer to validate generated policies. LeapLogic operates within customer AWS account boundaries, and customers retain full responsibility for their security configuration and compliance requirements under the AWS Shared Responsibility Model.

Conclusion

Modernizing legacy workloads to Amazon SageMaker can be a strategic step for enterprises seeking to scale ML adoption while reducing operational complexity. SageMaker provides a managed foundation for consistent training, deployment, and monitoring of ML models across the enterprise.With Impetus LeapLogic, organizations can approach this transformation with greater confidence. By automating key aspects of workload analysis, transformation, and validation, Impetus helps reduce migration risk and shorten time to value enabling enterprises to operationalize ML on AWS in a controlled, production ready manner.

As an AWS Premier Tier Services Partner with certified Machine Learning and Data & Analytics competencies, Impetus helps enterprises modernize and operationalize ML workloads on SageMaker with speed, accuracy, and operational discipline.

To learn more about how Impetus LeapLogic supports automated modernization of legacy workloads on AWS, contact Impetus.

Impetus Technologies – AWS Partner Spotlight

Impetus Technologies is an AWS Premier Tier Services Partner who enables the Intelligent Enterprise with innovative data engineering, cloud, and enterprise AI services. By helping enterprises modernize workloads and leverage cutting-edge AWS technologies, Impetus empowers businesses to innovate, streamline operations, and unlock new opportunities.

Contact Impetus Technologies | Partner Overview | AWS Marketplace

Unlock revenue insights in the new Attributed Revenue Dashboard

Jahnai Bilovsky — Tue, 21 Apr 2026 17:13:34 +0000

By Jahnai Bilovsky, Kelly Bornholdt, and Chris Xu – APN/MP Tech Business Development, AWS

As the cloud industry evolves, Partners have asked for more ways to demonstrate the business impact and return on investment of their solutions. Yet many lack visibility into the actual revenue impact their AWS solutions drive, making it difficult to optimize their go-to-market strategies.

To support this need, we launched Partner Revenue Measurement—a set of capabilities that help Partners measure attributed revenue, demonstrate impact, and unlock mutual growth opportunities.

Partner Revenue Measurement is now how we measure partnership success across Partner programs and benefits, creating a direct line between Partner impact and AWS investment. It provides transparent, data-driven measurement that recognizes Partners based on actual AWS service consumption across Partner and customer accounts.

Now, we’re excited to announce the Attributed Revenue Dashboard in AWS Partner Central, bringing Partner Revenue Measurement insights to life with automated revenue data by Partner product, AWS service, and billing period.

In this blog, we’ll cover the benefits of Partner Revenue Measurement, how the Attributed Revenue Dashboard works, and how to get started.

Benefits

Partner Revenue Measurement delivers precise, automated measurement of the AWS consumption you’re driving—eliminating guesswork and ensuring your full impact is recognized. This enables faster funding decisions with higher investment levels.

This data-driven approach transforms how Partners and AWS collaborate. Here’s what that means for your business:

Revenue impact visibility: See exactly which products and services are driving AWS consumption, with monthly data by Partner product and AWS service to inform strategic decisions.
Smarter co-selling: Demonstrate solution value with quantifiable metrics to AWS sales teams, helping you identify high-performing co-sell areas to focus your resources and drive growth.
Strategic partnership growth: Use performance data to drive business reviews with AWS, optimize your product and go-to-market strategies, and unlock more targeted investment and recognition based on actual impact.
Automation and faster payments: Reduce administrative burden and get the proof points you need to grow your AWS business—eliminating manual reporting, reconciliation, and tracking across multiple programs.
Customer cost management visibility:  Enable customers to identify Partner-provisioned resources and map costs for spend management.

How it works

The Attributed Revenue Dashboard, accessible through Partner Analytics in AWS Partner Central in the AWS Console, provides visibility into the revenue impact your solutions generate.

Figure 1 – Attributed Revenue Dashboard in AWS Partner Central.

The dashboard displays monthly attributed revenue by Partner product, AWS service, and billing period. View consumption patterns, workload-level performance metrics, and growth opportunities—all in one place.

Figure 2 – AWS attributed revenue by AWS service.

Partners can get started with Partner Revenue Measurement using one of three implementation capabilities: Resource Tagging, User Agent string, and AWS Marketplace Metering—providing flexibility to choose the one that works best for their solution architecture.

Resource Tagging: Apply standardized tags to AWS resources using the product code from your AWS Marketplace listing.
User Agent string: Measure the AWS revenue impact of your solution across certain services by embedding a unique product code as a user agent—with options to update application code directly or set an environment variable.
AWS Marketplace Metering: Automatically measures Amazon EC2 and Amazon SageMaker AI service consumption across customer-managed environments when customers purchase and use Amazon Machine Image (AMI) and machine learning (ML) products via AWS Marketplace. No additional implementation effort required.

Figure 3 – Partner product onboarding status by source.

What Partners are saying

Feedback from pilot Partners played a key role in helping us develop the dashboard and Partner Revenue Measurement capabilities. Partners tell us that Partner Revenue Measurement represents a fundamental shift in how AWS recognizes Partner value.

“We see Partner Revenue Measurement as a turning point. It aligns Pier Cloud and AWS on a shared goal: enabling customers to scale, modernize, and succeed with AWS,” says Marcelo Scharan, Founder & CEO, Pier Cloud.

Beyond the strategic value, Partners also appreciate the ease of implementation.

Dack Busch, VP Systems Engineering & Solution Architecture at Qumulo, shares: “This APN ID tag is so easy to implement and what I’ve wanted from AWS for several years to track the demand we drive as an AWS Partner for Amazon S3 and Amazon EC2 services.”

Get started

To access the Attributed Revenue Dashboard, Partners need to migrate to the new AWS Partner Central experience.

To get started with Partner Revenue Measurement:

Have a public listing in AWS Marketplace;
Link your AWS account to your AWS Partner Central account;
Implement Resource Tagging, User Agent String and/or AWS Marketplace Metering capabilities.

Partners with multiple AWS Marketplace seller accounts can use Subsidiary Account Connections to gain visibility of their entire solution portfolio across all seller accounts.

Learn more about the dashboard’s functionality and the latest Partner Revenue Measurement updates in the Onboarding Guide.

Accelerate customer experiences powered by Adobe and AWS agentic AI collaboration

Ranjith Raman — Mon, 20 Apr 2026 14:31:06 +0000

By: Ranjith Raman, Principal Solutions Architect – AWS
By: Anuj Dewangan, Principal Solutions Architect – AWS

Adobe

New product availability and integrations bring enterprise-grade agentic AI capabilities to marketers and developers, enabling seamless workflows across Adobe and AWS.

Adobe and Amazon Web Services (AWS) today announced a strategic collaboration to accelerate enterprise adoption of agentic AI, that enable businesses to build, deploy, and orchestrate AI agents across marketing and customer experiences. The announcements, made at Adobe Summit 2026, demonstrate the companies’ shared commitment to making agentic AI accessible and actionable for enterprises worldwide.

The collaboration introduces bidirectional integration between Adobe Experience Platform Agent Orchestrator and Amazon Bedrock AgentCore, along with a purpose-built Adobe Marketing Agent for Amazon Quick that transforms campaign insight discovery from weeks to seconds. Adobe Experience Platform Agent Orchestrator is now available on AWS, enabling customers to build and deploy intelligent AI agents at scale with the enterprise-grade security, performance and reliability of AWS. Together, these innovations enable enterprises to create sophisticated, cross-platform AI workflows that combine Adobe’s creative and marketing expertise with AWS’s cloud infrastructure and AI capabilities.

“At Adobe, we’re committed to empowering marketers and creatives with AI-driven insights and automation to deliver personalized customer experiences at scale. By bringing Adobe Experience Platform Agent Orchestrator to AWS and integrating our marketing intelligence into Amazon Quick, we’re enabling enterprises to uncover campaign insights in seconds—all while maintaining the enterprise-grade security and scale our customers demand,” says Ankur Jain, Director of Product Management, Ecosystem Solutions, Adobe. “This collaboration represents the next evolution of customer experience, where intelligent agents work seamlessly across platforms to deliver measurable business outcomes.”

Adobe Experience Platform Agent Orchestrator on AWS
Agent Orchestrator in Adobe Experience Platform (AEP) powers customer experience orchestration with purpose-built agents, an advanced reasoning engine, and a powerful conversational interface. Agent Orchestrator, available now on AWS, enables enterprises to deploy Adobe’s agentic AI capabilities with the security, scalability, and global reach of AWS cloud services.

Adobe Marketing Agent for Amazon Quick
Adobe’s AI Agents meet marketers where they are. Adobe Marketing Agent for Amazon Quick enables marketers and business practitioners to discover marketing campaign insights in seconds—a process that traditionally takes days or weeks. Adobe Marketing Agent brings Adobe’s marketing intelligence directly into Amazon Quick’s AI-powered workspace, allowing teams to query campaign performance, audience insights, and content effectiveness through natural language. We’re collaborating with select customers and partners in a private beta to validate scenarios and deployment models ahead of wider availability.

“The future of enterprise AI lies in intelligent agents that work together across platforms to solve real business problems. Through our collaboration with Adobe, we’re making it possible for developers and marketers to build sophisticated agentic workflows that combine Adobe’s deep marketing expertise with AWS’ cloud infrastructure and AI capabilities. With Experience Platform Agent Orchestrator on AWS and integrations with Amazon Quick and Amazon Bedrock AgentCore, we’re delivering the interoperability and scale that enterprises need to accelerate their AI transformation.” – Chris Grusz, Managing Director, Technology Partnerships, AWS

Adobe and Amazon will continue to innovate and bring new agentic integrations across their product portfolios that enable brands to deliver standout customer experiences.

Interacting with Adobe CX Enterprise from within Amazon Bedrock AgentCore Runtime
Developers will be able to access the agentic capabilities in Adobe CX Enterprise within Amazon Bedrock AgentCore Runtime through Model Context Protocol (MCP) servers. This will open new possibilities for developers to build specialized agentic workflows tailored to their unique use cases.

Amazon Bedrock AgentCore agents in Adobe
Agents hosted on Amazon Bedrock AgentCore Runtime will be accessible within Adobe Agent Orchestrator, enabling seamless cross-platform agent workflows. For example, marketers can invoke an agent hosted in Amazon Bedrock AgentCore, to run semantic searches on past campaign brief documents archived in Amazon Simple Storage Service (Amazon S3) directly from their Adobe workflows, combining Adobe’s customer experience agents with the data management capabilities of AWS.

Adobe Customer Experience Skills
Adobe Customer Experience Skills—pre-built capabilities for common marketing tasks—will be accessible from Amazon Bedrock AgentCore Runtime. Developers can incorporate these skills into their custom agents, accelerating development time and ensuring enterprise-grade quality for Adobe-powered workflows.

These collaborations build on the longstanding partnership between Adobe and AWS, which has enabled millions of customers to use Adobe’s creative and marketing solutions on AWS.

With these new agentic AI integrations, the companies are extending that partnership into the next era of enterprise AI, where intelligent agents work together across platforms to automate complex workflows and deliver actionable insights.

Learn more about the innovations that Adobe and AWS are driving together at Adobe Summit 2026.

Adobe – AWS Partner Spotlight

Adobe is an AWS Competency Partner that helps customers create, deliver, and optimize compelling content and applications.

Contact Adobe | Partner Overview

Cisco conversational AI powered by Amazon Lex

Satish Subramanian — Wed, 08 Apr 2026 21:58:56 +0000

By: Aaron Keeton, Business Development Manager – Cisco
By: Jessica Smith, Director Product Marketing – Cisco
By: Soundarya Muthuvel, Product Manager Webex Developer Experience – Cisco
By: Ravi Thakur, Sr. Solutions Architect – AWS
By: Satish Subramanian, Manager Partner Solutions Architects – AWS

When customers call your support line, they’re not thinking about the technology behind it. They want answers – fast, accurate, and without the runaround.

But contact center leaders face a familiar challenge: you’ve invested in Cisco Webex Contact Center, your teams have expertise around it, and it delivers the experience your customers want. Meanwhile, conversational AI is reshaping what customers expect-instant responses, natural conversations, and uninterrupted availability without waiting in line. The pressure to modernize is real but ripping out what’s working isn’t a beneficial strategy.

That’s why Cisco and Amazon Web Services (AWS) took a different approach. They brought AWS AI capabilities into the Cisco environment, so you can modernize without starting over.

The new open source Amazon Lex connector for Webex Contact Center brings intelligent virtual agents into your existing Cisco environment, without rebuilding from scratch. Amazon Lex now plugs directly into Cisco’s Bring Your Own Virtual Agent (BYOVA) framework. With the Amazon Lex integration, you can access a powerful AI-driven virtual agent alongside your contact center workflows without any additional work, providing consistency across voice experiences. Your customers get natural, voice-driven interactions. Your team retains their existing Webex workflows, reporting dashboards, and agent desktop configurations while gaining AI capabilities that would otherwise require months of integration work.

For the more than 10,000 organizations running Cisco Webex Contact Center and Contact Center Enterprise today, this solution removes the trade-off between protecting your investment and adopting AI. You can deploy across many AWS Regions globally, and it supports over 25 languages and automates routine inquiries so that your agents can focus on conversations that really need a human.

Here’s how the integration works, where it fits across Cisco Webex Contact Center and Cisco Contact Center Enterprise, and where customers are already seeing results.

Cisco and AWS integration architecture

This integration connects your Cisco Webex Contact Center to AWS AI services through an open-source connector. The architecture comprises three integrated layers:

Customer interaction layer – Webex Contact Center and Contact Center Enterprise connect through Webex Contact Center AI (CCAI), which manages the virtual agent integration and handles escalations routing to an agent when human assistance is needed.
Connector layer – The open-source connector serves as the technical bridge between platforms, authenticating between Webex and AWS environments, managing real-time gRPC media streams for audio, and orchestrating the conversation flow between platforms.
Conversational AI layer – Amazon Lex provides conversational intelligence through automatic speech recognition (ASR), natural language understanding (NLU), and integration with large language models (LLMs) using Amazon Bedrock. Speech synthesis is handled by Amazon Polly, delivering natural-sounding responses in dozens of voices across multiple languages.

This architecture is illustrated in the following diagram.

Figure 1: Cisco Webex Contact Center integration with AWS AI services

Customer call flow

When a customer calls in to the Webex Contact Center, the following flow takes place:

Audio streams in – The caller’s voice flows from Webex Contact Center through Webex CCAI to the open-source connector. The connector establishes a gRPC media stream to carry the audio in real-time to AWS services. This stream also enables bidirectional metadata exchange, including caller information, historical journey, and subscription data between Webex CC and AWS.
Amazon Lex interprets intent – Amazon Lex processes the customer’s speech through three stages:
1. Speech recognition – ASR converts voice to text, supporting more than 25+ languages
2. Intent understanding – NLU determines what the customer wants, now enhanced with LLM-powered assisted NLU for faster, description-based intent classification
3. Response generation – For straightforward requests, Amazon Lex responds from configured intents. For complex queries requiring nuanced responses, Amazon Lex invokes Amazon Bedrock foundation models (FMs) to generate contextual, conversational replies that go beyond scripted answers.
Response flows back – Amazon Lex formulates the response and passes it to Amazon Polly. Amazon Polly transforms text into natural-sounding speech using neural text-to-speech (NTTS) technology, with support for Speech Synthesis Markup Language (SSML) to control pronunciation, emphasis, and speaking rate. The audio streams back through the connector to the customer using Webex Contact Center.
Escalation when needed – If the virtual agent can’t resolve the issue, the call routes back through Webex CCAI to a live agent within your Cisco Webex Contact Center environment. The agent receives full conversation context—including customer intent, information gathered, and dialog history—eliminating the need for customers to repeat themselves.

This architecture supports multi-turn conversations with contextual awareness requests like “I want to change my appointment from Tuesday to Thursday,” which work without requiring customers to start over.

The customer call flow is illustrated in the following flow diagram.

Figure 2: Cisco Webex Contact Center and Amazon Lex integration call flow

This solution integrates with AWS in the following ways:

AI/ML services – Amazon Lex handles ASR and NLU, converting customer speech to text and determining intent. Amazon Polly delivers lifelike voice responses using Neural Text-to-Speech (NTTS).
Agentic developer tools – Amazon Bedrock AgentCore provides the framework for building and orchestrating AI agents.
Generative AI with Amazon Bedrock – For more complex interactions, Amazon Bedrock connects your virtual agent to FMs, enabling responses that go beyond scripted intents.

The value of this architecture lies in the control it gives you. The Amazon Lex chat assistant lives in your AWS account. You control the intents, responses, and data. The solution is designed for responsive, natural conversations. Security is through cross-account authentication, which means your customer data stays in your environment. This solution is available in AWS Regions that support Amazon Lex endpoints to meet your compliance and data residency requirements.

Industry specific use cases and key benefits

Organizations across industries can find benefits in pursuing this approach, including in healthcare, finance, and government.

Healthcare organizations can enhance patient experience with intelligent routing and reduced wait times by consolidating outdated systems that need a live agent for every task. The solution is integrated with scheduling systems for seamless appointment management using automated booking, rescheduling, or canceling.

Finance organizations can improve customer self-service in situations where they don’t need to talk to a live agent by using intelligent interactive voice response (IVR), chat-based assistants, and virtual agents. Customers can check balances, update personal details, or reset passwords without a live agent. Reduce complexity through unified customer experience platforms and cloud-based solutions.

Governments can operate in Cisco’s secure, FedRAMP-authorized data centers for reliability and compliance. This solution enables migration from on premises to the cloud at the agency’s pace with an intuitive administration portal for faster operations and reduced IT burden.

Business outcomes

Conversational AI transforms routine customer interactions, including billing inquiries, order tracking, and appointment scheduling into seamless self-service experiences. Customers get immediate responses without waiting in queue, while your contact center achieves higher first-contact resolution rates. The result is consistent, quality support available around the clock without proportional staffing costs.

	Step	Traditional process	With Amazon Lex integration
1	Customer initiates	Waits in queue (4–8 min average)	Immediate virtual agent response using Webex Contact Center
2	Authentication	Agent manually verifies (2–3 min)	Amazon Lex authenticates conversationally (30 seconds)
3	Resolution	6–8 minute handle time	60–90 seconds for routine inquiries

Similar efficiency gains apply to order status and appointment scheduling interactions.

Natural language understanding in Amazon Lex goes beyond recognizing what customers say, it understands the intent of the customers. By connecting to customer data, the virtual agent delivers contextual responses. It recognizes repeat callers, references recent interactions, and anticipates needs. This intelligent, empathetic approach builds loyalty by making every customer feel known, not processed.

Conclusion

In today’s Cisco Webex Contact Center deployments, organizations face a defining moment. The need to address how to safeguard existing investments and resources but adopt AI capabilities to get natural, voice-driven interactions. Through the open-source Amazon Lex connector for Webex Contact Center, we’ve demonstrated how customers can bring powerful AI-driven intelligent virtual agents into their existing Cisco environment without significant investments or re-architecture.

Organizations implementing this open-source Amazon Lex connector show improved AI agent efficiency and enhanced customer experience. This solution integrates with proven AWS AI capabilities to improve every interaction and maintain context and intent across complete conversations, improving your customer experience.

Refer to this blog for step by step instructions on how to integrate Webex Contact Center with open-source Amazon Lex connector.

Ready to transform your customer experience strategy? Connect with Cisco and your AWS account team today to schedule a personalized consultation and discover how this integration drives customer satisfaction and business growth for your organization.

Cisco Systems – AWS Partner Spotlight

Cisco Systems is an AWS Specialization Partner that helps customers optimize their cloud strategy by bringing together networking, security, analytics and management.

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