An agent feedback loop is the operating cycle that turns production behavior into measurable improvement. A typical loop is: capture traces, run evals, analyze failures, curate datasets, test changes, compare experiments, and deploy only when the evidence supports it.
The loop gives teams a way to answer the question every production agent raises: did this change make the system better or worse? For developers, that means evals should be wired into the same places code quality already lives: local development, pull requests, CI, staging, production monitoring, and incident response.