Closed-loop agents are agents connected to a feedback loop: observe what happened, evaluate whether it was good, improve the system, and deploy the change under policy. The loop matters because agent quality is not fixed at launch. Prompts drift, tools fail, retrieval changes, user behavior changes, and model upgrades can improve one behavior while breaking another.
A closed-loop agent does not necessarily deploy changes autonomously. The "closed" part means production behavior flows back into development through traces, evals, datasets, experiments, and deployment gates. Teams can decide where humans approve, where automation runs, and where policy blocks unsafe changes.