Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore
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This article details a production-ready evaluation framework for AI agents developed by AWS to ensure trust and reliability in enterprise settings. The authors address the critical challenge of 'soft failures,' where agents provide fluent but incorrect answers that can damage user relationships and business decisions. To solve this, they implemented a nine-stage continuous quality loop using Amazon Bedrock AgentCore, covering everything from initial definition to live monitoring. Key stages include establishing a golden dataset, running CI/CD regression gates, conducting canary tests for silent drift, and enforcing safety guardrails against adversarial attacks. The framework utilizes both deterministic checks and LLM-as-judge evaluators to measure accuracy, grounding, and hallucination rates across tool, agent, and multi-agent levels. By integrating these evaluations into CloudWatch and Quick dashboards, the team reduced mean time to detection from weeks to under four hours. Ultimately, the approach treats evaluation as the operating system of the agent, enabling automatic defense and continuous improvement through automated feedback loops.
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