Agent Feedback Loop

A mechanism where outcomes of an AI agent's actions—user ratings, task success/failure, correction data, and downstream metrics—are fed back to improve the agent's future performance. Feedback loops power continuous improvement through prompt refinement, retrieval tuning, eval set expansion, and fine-tuning. Without them, agents are static; with them, agents improve with every interaction. The loop can be automated (auto-add failed cases to evals) or human-driven (analysts review and correct agent outputs).