Amazon Bedrock AgentCore Evaluations tests multi-agent systems for explainability and accuracy
Amazon Bedrock AgentCore Evaluations introduces a framework for assessing multi-agent systems in production. The platform helps enterprises evaluate agent performance across quality dimensions like helpfulness, task success, and explainability, beyond just response quality. It supports both built-in evaluators for general metrics and custom evaluators for domain-specific checks, such as…
Key points
- AgentCore Evaluations combines built-in and custom evaluators to assess task success, explainability, and business rule adherence
- Custom evaluators validate domain-specific constraints like budget limits, inventory coverage, and route feasibility in supply chain use cases
- Explainability checks independently measure transparency, distinguishing accurate-but-unclear recommendations from well-reasoned ones
The solution demonstrates a three-layer evaluation approach using a fictitious retail company, AnyCompany Retail. It deploys an orchestrator agent and four specialized sub-agents—optimization, distribution, routing, and analytics—to optimize inventory allocation, distribution, and logistics. Evaluations include built-in metrics like Helpfulness and Tool Selection Accuracy, alongside custom checks for business rules. Explainability evaluators ensure agents articulate decision rationale, cite supporting data, and clarify trade-offs. The framework supports both on-demand and online evaluation modes, with results streamed to Amazon CloudWatch dashboards. The post provides a GitHub repository with deployment instructions and sample queries for testing.
Evaluating multi-agent systems for explainability and helpfulness with Amazon Bedrock AgentCore
AWS Machine Learning Blog · 5 October 2026
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