AWS tutorial: evaluating multi-agent systems for explainability with Bedrock AgentCore
AWS ML Blog · rss · 2026-10-05
AWS explains how to evaluate multi-agent systems for accuracy and explainability with Amazon Bedrock AgentCore Evaluations. Enterprise agents can't be judged on response quality alone; correctness depends on tool selection, workflow execution, and adherence to business constraints.
The walkthrough uses a fictitious retailer, AnyCompany, building a supply-chain decisioning system with Strands Agents SDK: an orchestrator agent plus optimization, distribution, routing, and analytics sub-agents, tools wired via AgentCore MCP Server, with Memory and Observability enabled.
Evaluation follows three layers: built-in evaluators (Helpfulness, Tool Selection Accuracy, Instruction Following) as baseline; custom evaluators encoding business rules (constraint satisfaction, route feasibility, SQL correctness, inventory grounding, plan coherence); and a dedicated explainability layer checking whether agents articulate decision rationale, cite supporting data or tool outputs, and explain tradeoffs like cost vs. service level. Supports on-demand mode (benchmarking, regression, CI/CD gates) and online mode (sampling production traces, auto-scoring to CloudWatch dashboards and alarms).
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