AWS Partner Network (APN) Blog

Agentic AI represents the third major platform shift in enterprise software, transforming how SaaS is built, priced, distributed, and consumed by a hybrid workforce of humans and autonomous agents. Learn how ISVs can capture this growth opportunity by building on Amazon Bedrock AgentCore for production-ready agentic deployments, making their capabilities discoverable through open protocols such as MCP and using AWS Marketplace for outcome-based pricing models that capture the measurable value agents create.

New agentic capabilities in AWS Partner Central support you at every stage of your selling journey—from identifying the right leads to getting real-time insights and support on every deal you share with AWS. Learn how to build a stronger pipeline, accelerate deal progression, and get real-time qualification on every opportunity.

We’re expanding agentic capabilities in AWS Partner Central that get you ready to sell in days—from automated profile setup and AWS Marketplace listing creation to streamlined Foundational Technical Review validation. Now you can now unlock badging, benefits, and funding faster.

We’re announcing the Amazon Connect Customer Competency, helping customers find validated Partners who can migrate legacy contact centers and build AI-powered customer experiences on Amazon Connect. Benefits include $50K in Marketing Development Funds, AWS Migration Acceleration Program funding, and dedicated co-selling support to accelerate customer transformations.

Customers are shifting toward outcome-based engagements—and they’re willing to pay a premium for Partners who can prove results. We’re introducing the Business Value Realization (BVR) motion, which provides Services Partners with funding, enablement resources, and specialized support to lead post-sales customer success.

Open-weight large language models are being released by research institutions worldwide, but turning published weights into production inference services remains a challenge—especially under strict data residency requirements. This post shows how Public AI built a scalable inference platform on Amazon EKS and Intel-powered Amazon EC2 instances to serve Switzerland’s Apertus model family, and why this architecture provides a repeatable blueprint for sovereign LLM initiatives.

AI coding assistants and MCP servers have made development faster, but they’ve also made secrets exposure harder to catch. Developers share credentials through config files, Git repos, and CI/CD logs without realizing it. This post walks through how GitGuardian integrates with AWS Secrets Manager to give security teams full visibility across the secrets lifecycle: detecting when vaulted credentials show up in code, finding duplicate secrets scattered across multi-account architectures, and putting continuous governance policies in place so secrets management becomes proactive rather than reactive. We cover a phased implementation roadmap, from initial deployment through automated monitoring, that helps you build a secrets security strategy that grows with your organization.

Organizations accumulate thousands of hours of session recordings that satisfy compliance mandates but rarely get reviewed. Learn how Teleport and Amazon Bedrock replace manual playback with AI-powered summarization, risk classification, and SIEM-ready alerts—keeping session data within your AWS environment.

Discover how hybrid elasticity introduces a zero-migration data access model that decouples data residency from compute elasticity, enabling enterprises to transform existing data centers into dynamic hybrid data hubs with on-demand cloud scale without moving data.

Most AI systems today have no direct understanding of the physical world because the underlying spatial data requires purpose-built preprocessing before AI can use it. By using Wherobots, built on AWS, you can transform petabyte-scale spatial data in Amazon S3 into context that AI can reason about through distributed inference, a spatial query engine, and agentic AI integrations. Learn how customers like Leaf Agriculture, SatSure, and Aarden use Wherobots on AWS to operationalize earth observation data, accelerate spatial processing by up to 300 times, and build AI systems grounded in physical reality.