AWS Details Agent Evaluation Architecture on Amazon Bedrock AgentCore

AWS outlined an evaluation framework for production AI agents using Amazon Bedrock AgentCore Evaluations. The framework addresses the operational challenge where standard system metrics, such as latency or completion counts, fail to capture issues like step omission, context loss, or sensitive data leakage. AgentCore provides a three-tiered catalog comprising AWS built-in evaluators, managed third-party evaluators from DeepEval and AutoEval, and custom LLM-as-a-judge evaluators. The system supports multi-dimension evaluations where a single judge call scores several distinct criteria, alongside on-demand and online evaluation execution modes.

Key takeaways

• Bedrock AgentCore provides built-in metrics covering response quality, goal completion, tool use, skills, and ground-truth trajectory matches.
• Managed third-party integrations include DeepEval for conversational quality and safety, as well as AutoEval for security prompt-injection and refusal checks.
• Teams can deploy multi-dimension custom evaluators to score distinct business criteria from a single trace without conflating independent failure modes.
• Evaluations run across execution modes including on-demand calls via client.evaluate() for testing and online runtime sampling for live production monitoring.