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163 summarised stories about AWS, each linking back to the original source. Browse all topics →

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Thursday, 20 August 2026

Scaling agentic AI: Enterprise patterns without vendor lock-in

Amazon Web Services 1 month ago 5 ● 5 sources

The post outlines architectural patterns for scaling agentic (multi-agent) AI across enterprises without locking teams to a single framework or model provider. It is Part 2 of a series on multi-agent systems at scale. It recommends standardizing below the application layer with shared control-plane capabilities (like identity, policy, observability, and routing) and using AWS services—especially SageMaker for customization/inference and Bedrock for foundation-model access—to reduce fragmentation while letting execution remain flexible.

Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore

Amazon Web Services 1 month ago 15 ● 5 sources

AWS Professional Services describes a multi-agent setup on Amazon Bedrock AgentCore that applies agentic AI across application discovery, infrastructure-as-code generation, governance reporting, and post-migration operations. The agents reduced infrastructure-as-code development time from 3–4 weeks per application to minutes across a portfolio of over 300 applications. As a result, migration teams shift repetitive work from engineers to the agents while keeping decision authority, and post-migration operations move toward proactive monitoring and automated remediation instead of reactive firefighting.

AWS vector solutions: Build agentic AI where your data lives

Amazon Web Services 1 month ago 31

AWS is positioning “AWS vector solutions” as a way to add retrieval for agentic AI without moving or duplicating data across systems. The announcement highlights that Amazon OpenSearch Serverless autoscales 20x faster than its previous generation and can provision in seconds. As a result, teams can choose among different AWS vector engines (including OpenSearch, S3 Vectors, and DynamoDB vector search) to match latency, cost, and access patterns for RAG and semantic retrieval workloads.

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