Industry story
AgentCore Launches Agent Performance Loop: Optimization, Batch Eval, User Simulation
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AWS just shipped the missing half of the production agent stack. AgentCore's new optimization, batch eval, and user simulation capabilities close the loop that most teams are currently closing with spreadsheets and prayer — analyzing live traces to rewrite system prompts, replaying historical sessions to catch regressions, and spinning up LLM-backed actors to surface behavior no scripted test case would ever find. The question now is whether teams actually trust automated prompt rewrites enough to let them run, or whether human review turns this into a slower version of what they already do.
Full analysis
AWS AgentCore introduced three capabilities to close the observe-evaluate-optimize-deploy loop for production agents. Optimization analyzes production traces and evaluator outputs to recommend targeted updates to system prompts and tool descriptions, with built-in A/B testing. Batch evaluation replays historical sessions to catch regressions before deployment. User simulation generates realistic multi-turn conversations using LLM-backed (large language model) actors to expose behaviors beyond scripted test cases. Together these features let teams continuously improve agent quality using real production data rather than manual intervention.
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