Evaluating multi-agent systems for explainability and helpfulness with Amazon Bedrock AgentCore
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This article details a framework for evaluating multi-agent systems using Amazon Bedrock AgentCore, focusing on explainability and helpfulness in production environments. It addresses the challenge of ensuring agents are not only accurate but also transparent and aligned with business constraints. The solution uses a fictitious retail company scenario to demonstrate a three-layer evaluation approach: built-in evaluators for general quality, custom evaluators for domain-specific accuracy, and cross-cutting explainability checks. Key components include an orchestrator agent coordinating specialized sub-agents for optimization, distribution, routing, and analytics. The system employs both on-demand evaluations for development and online monitoring for continuous production oversight. Custom evaluators verify specific metrics such as constraint satisfaction, route feasibility, and SQL correctness. Explainability evaluators ensure agents articulate decision rationale, cite evidence, and disclose assumptions. This layered strategy allows teams to distinguish between correct but opaque decisions and well-explained but flawed ones.
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