Concept
Agent loop
An agent loop is the control cycle an LLM agent runs to accomplish a task: observe the current state, decide on an action, execute it, then feed the result back in and repeat until a stop condition. If you know the classic reinforcement learning loop (`observe -> act ->…
The rest of “Agent loop” is a premium feature: every concept in the library gets a precise, practitioner-focused write-up like this one, cross-linked straight from the paper summaries that use it.
Log in to unlock→