Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore

This article presents a comprehensive nine-stage evaluation framework for production AI agents using Amazon Bedrock AgentCore, addressing how to maintain quality and trust at scale.

• Define quality metrics upfront by translating business outcomes into measurable evaluation criteria across five dimensions: safety, quality, performance, cost, and operational control
• Use multi-type evaluator stacks combining AgentCore built-in evaluators, custom domain-specific evaluators, third-party evaluators, and deterministic ground-truth checks
• Implement CI/CD gates with sentinel sets to catch regressions before production, using sandboxed tool execution and mocking to isolate agent reasoning quality
• Deploy continuous canary monitoring with 30-minute evaluation windows to detect silent quality degradation from upstream data changes and model drift
• Apply Amazon Bedrock Guardrails at input and output to protect against prompt injection, jailbreaks, and unauthorized data access on 100% of traffic
• Use online evaluation on live traffic to validate candidates against real-world query patterns before full rollout via A/B testing
• Establish real-time observability through AgentCore Observability and CloudWatch integration, reducing mean time to detect issues from days to under four hours
• Close feedback loops with AgentCore optimization to generate data-driven system prompt and tool description recommendations based on failure patterns

The framework treats evaluation as the operating system of agents themselves, enabling continuous measurement, automatic defense against regressions, and improvement with every interaction across tool, agent, and multi-agent levels.