AWS Partner Network (APN) Blog

We’re excited to bring you AWS re:Invent 2026 both in-person and virtually! The conference is November 30 – December 4 in Las Vegas, with virtual opportunities to watch keynotes and Innovation Talks live. To help you prepare, we curated a list of key re:Invent information for AWS Partner and AWS Marketplace communities.

Demand forecasting works at catalog scale, but value still leaks in the last mile, where a forecast must become a placed order. This post describes a pattern that closes that gap: Databricks Many Model Forecasting and a Genie Agent surface the forecast, and Amazon Quick reconciles it against an external supplier feed, placing routine orders automatically and escalating only the exceptions. The pattern applies well beyond supply chain.

Managing AI costs is a top challenge for organizations scaling generative AI on AWS. This post shows how AWS CUR 2.0, Amazon S3, and AWS Trusted Advisor form the data foundation that New Relic Cloud Cost Intelligence builds on for Amazon Bedrock workloads. The result is token-level cost visibility inside the observability workflows engineering teams already use, shifting AI from opaque experimentation to disciplined, ROI-driven operations.

AI growth in regulated environments put pressure on the systems that govern your data. You need to give teams timely access to approved datasets without losing control of data placement, lineage, or reproducibility. In this post, we share three data architecture principles, show how Domino Data Lab applies them with Amazon FSx for NetApp ONTAP, and give you workload-level metrics to evaluate the architecture.

Banking, financial services, and insurance (BFSI) institutions need AI systems that can act autonomously at market speed, but autonomy without governance is a nonstarter in an industry defined by regulatory intensity and operational risk. In this post, Apexon and AWS present an architecture-first approach to operationalizing agentic AI on AWS, with governance built in from the start across three layers: intelligence, orchestration and execution, and governance, security, and audit. Learn how governed agentic patterns apply to core BFSI workflows like customer onboarding, credit processing, and payments exception handling, enabling autonomy at scale while keeping every action traceable and auditable.

We are excited to highlight 137 AWS Partners that received new or renewed specializations in October for our global AWS Competency and AWS Managed Service Provider (MSP) programs. These designations span workload, solution, and industry, and help AWS customers identify top AWS Partners that can deliver on core business objectives. AWS Partners are focused on your success, helping customers take full advantage of the business benefits AWS has to offer.

AI agents authenticate, hold permissions, and act on data, yet most organizations manage them as application components rather than governed identities. Learn how Saviynt Zuma works with Amazon Bedrock AgentCore to govern agents as first-class identities, from registration and least-privilege provisioning through runtime authorization, certification, and deprovisioning. Security teams can then answer which agents exist, who owns them, and what they can access.

The AWS Direct Connect Partner Program replaces the AWS Direct Connect Service Delivery Program with a two-tier path that opens partner validation to new and emerging AWS Regions. Partners can now join at V1 on the strength of their technical capability, before they have customer case studies, and move up to V2 as they win customers. A dedicated validation track also brings validated partners to the AWS European Sovereign Cloud. Read on to learn what changed, how the tiers compare, and how to apply.

The race to deploy agentic AI is outpacing the governance needed to put it into production – 74% of companies plan to deploy by 2027, yet only 21% have mature governance models. AWS launched GEAR (Governance Enabling Agentic Readiness) to close that gap, identifying partners whose governance accelerators compress months of setup into weeks and turn AI pilots into production-ready systems with measurable results.

TEKsystems Global Services (TGS) helped a large media and entertainment enterprise transform their Amazon Connect Customer automated evaluation system from 66.67 percent accuracy to 100 percent. They developed a strategic classification framework that matches each evaluation question to the optimal automation method, either generative AI, conversational analytics categories, or contact metrics. This layered approach eliminated AI hallucinations, enabled full interaction coverage, and turned quality assurance from an unreliable manual process into a trusted, production-ready system that delivers actionable coaching insights at scale.

As generative AI adoption accelerates, the gap between deploying automation and making it trustworthy is becoming the critical differentiator. The next frontier of multi-modal insights and real-time coaching starts with the right foundation.