Evaluating multi-agent systems for explainability and helpfulness with Amazon Bedrock AgentCore

This article demonstrates how to build and evaluate multi-agent supply chain systems using Amazon Bedrock AgentCore Evaluations, combining built-in and custom evaluators to ensure agents are helpful, accurate, and explainable in production.

• Multi-agent systems require evaluation beyond response quality, including tool selection, workflow execution, and constraint adherence
• Three-layer evaluation framework: built-in evaluators for general quality, custom evaluators for domain-specific business rules, and explainability evaluators for transparency
• Reference architecture uses orchestrator agent with four specialized sub-agents (optimization, distribution, routing, analytics) for supply chain decisioning
• Custom evaluators validate business validity: constraint satisfaction, data grounding, route feasibility, SQL correctness, and plan coherence
• Six explainability evaluators assess decision rationale, evidence attribution, constraint reasoning, trade-off explanation, tool-use clarity, and assumption disclosure
• Solution supports on-demand evaluation for development and online monitoring for continuous production assessment
• Layered approach enables targeted improvements by distinguishing accurate-but-unexplainable from well-explained-but-wrong recommendations

The evaluation framework enables enterprises to systematically validate multi-agent systems across quality dimensions, establish production readiness gates, and deliver transparent, business-aligned agentic applications.