Prompt: Evaluating Multi-Agent Systems for Explainability and Helpfulness Using Amazon Bedrock AgentCore

This article shows how to assess multi‑agent supply‑chain assistants with Amazon Bedrock AgentCore Evaluations, combining built‑in metrics (helpfulness, task success) with custom checks for constraint satisfaction, grounding, and explainability to measure both accuracy and transparency.

Copy This Prompt

Evaluate the agent session {session_id} using the explainability evaluator {evaluator_arn} and return a score plus feedback on decision rationale, evidence attribution, constraint reasoning, trade‑off explanation, tool‑use explainability, and assumption disclosure.

Example input

Use the placeholders in the prompt with permitted, anonymized source data.

Expected output

Follow the output format requested in the prompt and verify the result against the source.

Tools

AWS CLI, AWS SAM CLI, Docker, Node.js, Python, Strands Agents SDK, strands-agents-tools, requests library, bedrock-agentcore package, boto3, Terraform