Industry story
AgentCore Launches Agent Performance Loop: Optimization, Eval, and Simulation
ai-in-adtech engineering measurement
AWS AgentCore just shipped the infrastructure that makes production AI agents actually improvable at scale: automated trace analysis that recommends prompt and tool description updates, A/B testing to validate those changes, batch regression evaluation against historical sessions, and LLM-backed user simulation to find edge cases before users do. That's the full observe-evaluate-optimize-deploy loop, closed. The question was never whether agents needed this — it was who would own the tooling. AWS just made a strong bid.
Full analysis
AWS AgentCore released three capabilities to close the observe-evaluate-optimize-deploy loop for production agents. Optimization analyzes production traces to recommend targeted updates to system prompts and tool descriptions, with built-in A/B testing to validate changes before rollout. Batch evaluation replays historical sessions to catch regressions, and user simulation generates realistic multi-turn conversations using LLM-backed actors to surface edge-case behaviors. Together these tools allow teams to continuously improve agent quality using real production data rather than manual intervention.
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