An AI improvement loop is the broader process of using real system behavior to improve an AI application. It can apply to a chatbot, RAG pipeline, classifier, workflow agent, or multi-agent system. The core pattern is the same: observe, evaluate, diagnose, change, rerun, compare.
The term is useful when the system is not strictly an agent. A RAG app might improve through better chunking, retrieval, prompts, or evaluation rubrics. An agent might improve through tool policies, planning constraints, checkpointing, or orchestration changes.