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AWS shares a concise enterprise checklist for AI agents with Bedrock AgentCore

AWS lays out a focused set of engineering practices for production AI agents using Amazon Bedrock AgentCore, emphasizing scoped use cases, observability, tooling discipline, and measurable evaluation targets.

AWS outlines a nine-point enterprise playbook for building and scaling AI agents on Amazon Bedrock AgentCore. citeturn0view0

  • The guidance starts with narrowly scoped agent definitions and ground‑truth datasets, calling out concrete deliverables such as clear scope boundaries, explicit tool definitions, and expected interaction sets. citeturn0view0
  • AgentCore services emit OpenTelemetry traces by default and pair with Amazon CloudWatch Generative AI observability dashboards for production monitoring and debugging. citeturn0view0
  • For tool integration, the post highlights MCP servers from services like Slack, Google Drive, Salesforce, and GitHub, and recommends using AgentCore Gateway to unify internal and external tools behind one protocol. citeturn0view0
  • Example evaluation targets include 95% tool‑selection accuracy, 98% parameter‑extraction accuracy, and 100% refusal accuracy, plus latency goals (P50 under 2 seconds, P95 under 5 seconds) and token usage under 5,000. citeturn0view0
  • A model‑swap example shows measurable tradeoffs: switching from Amazon Claude 4.5 Sonnet to Claude 4.5 Haiku improves latency (3.2s to 1.8s P50) but drops tool‑selection accuracy (92% to 87%). citeturn0view0