Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence.
AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement process still looks the same: without automatic feedback loops, when a user complains, a developer reads through traces, forms a hypothesis, rewrites the prompt, tests a handful of cases, and ships the fix. Then the cycle repeats, often introducing a new issue for a different user. Up until today, Amazon Bedrock AgentCore provided the pieces for you to debug it manually or build custom implementations: check the evaluation scores to detect quality drop, deep dive into the traces to determine the root cause and update the agent with an improved configuration. The developer is the performance engine relying on…
