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An AWS post demonstrates a Python AWS CDK application that deploys an agentic retrieval system using Managed Amazon Bedrock Knowledge Bases and AgentCore. The sample routes queries across two knowledge bases, uses the AgenticRetrieveStream API for multi-turn, cited answers, and provisions CloudWatch dashboards and on-demand/continuous evaluation for observability.
Eon implemented a centralized authenticated gateway using Amazon Bedrock AgentCore (Gateway, Runtime, Policy, Identity) to replace per-tool credentials and local MCP processes, giving engineers and autonomous agents unified access to investigation tooling across AWS, GCP, and Azure. The change reduced developer onboarding for investigation tooling from about two days to roughly two minutes and lets Eon's autonomous SRE agent (Claude on Bedrock) access the same tools with separate identities and permissions.
AWS published a how-to showing an AI agent system built on Amazon Bedrock AgentCore that automates generating client PowerPoint presentations from enterprise documents. The solution uses a supervisor agent (AgentCore runtime + Strands Agents) with Anthropic Claude Sonnet 4.6 for content, Amazon Titan Text Embeddings v2 and Amazon OpenSearch Serverless for RAG vector search, Stability AI Stable Image Inpaint for images, a .NET 8 REST API and AWS Lambda for generation, and a public code repository; internal testing completed in under 90 seconds.
AWS published guidance for the customer-managed configuration of Amazon Bedrock Knowledge Bases, comparing three supported vector-store backends—Amazon OpenSearch Service (managed and serverless), Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors—and evaluating their tradeoffs across RAG use cases (latency, cost, scalability). The post maps each backend to example workloads such as e-commerce product search and discusses performance and cost implications.
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