AWS shows how to evaluate the helpfulness and explainability of multi-agent systems
AWS describes an approach to evaluating multi-agent systems using Amazon Bedrock AgentCore Evaluations. In a supply chain example, it combines built-in quality checks with custom checks on business rules and decision rationales.
AWS published a guide to evaluating multi-agent systems using Amazon Bedrock AgentCore Evaluations. The example for a fictional retail company uses Strands Agents SDK and Amazon Bedrock AgentCore MCP Server. A coordinating agent distributes requests among four specialized agents for optimization, distribution, route planning and analysis. The system also uses simulated REST APIs in Amazon API Gateway; this is a reference implementation, not a documented deployment at an actual retailer.
According to AWS, built-in evaluators assess helpfulness, task completion and adherence to instructions. Custom evaluators in the example check compliance with constraints, route feasibility, SQL correctness, whether responses are supported by inventory data, and the quality of explanations. Explainability checks examine whether the agent gave reasons for its decisions, referenced supporting data or tool outputs, and explained trade-offs, such as those between cost and service level.
Evaluation takes place after task execution. On-demand mode is used for comparisons during development, regression tests and CI/CD checks; online mode is used for ongoing production monitoring and alerts. Amazon Bedrock Guardrails complements this approach with safety constraints during task execution. Details are available in the source article.
Why it matters
For a system that selects tools and performs several steps, the clarity of its response alone is not enough to assess whether its decisions are correct. The guide shows how to check specific operational constraints and whether recommendations are supported by data, alongside overall quality. It also distinguishes evaluation after task execution from safety checks during task execution.
Two audiences, two different impacts
What this means
For individuals
When testing their own system, developers can evaluate not only the final response but also whether the agent explained its decisions and supported them with data or outputs from the tools it used.
For a business
For a company running agents in its supply chain, the approach described connects checks on business constraints with regression tests before deployment and ongoing production monitoring. However, the example does not demonstrate savings or a reduction in error rates in actual operations.
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Event sources
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