Concepts & capability filters
agentsintermediate
Agentic loop
The agentic loop is the repeating cycle at the heart of every autonomous AI system: model → tool call → result → model → tool call → result → … until the model produces a final answer or the harness stops it.
- Category
- agents
- Difficulty
- intermediate
- Aliases
- None tracked
- Last reviewed
- 2026-04-21
Key facts
- On each iteration, the harness sends the model its current context, the model responds either with a tool call or a final message, the harness executes any tool call and feeds the result back into context, and the loop continues.
- This loop is what distinguishes agentic systems from simple chat.
- A chat completion is one turn — prompt in, response out.
- An agentic loop lets the model gather evidence, act, observe the effect, and revise its plan, turn after turn.
- It is how an AI actually does things — edits files, runs tests, searches, deploys — rather than just describing what it would do.
- Key design questions around the loop include: how many iterations to allow before escalating, how to compact context as history grows, how to detect when the model is stuck in a tool-call loop, how to handle errors from tool results, and when to hand control back to a human.
- Most harnesses expose configuration for all of these.
- The loop is executed by the harness; the steps taken within the loop depend on the agent's role, mission, and scope; and the quality of each step depends on the model and the context it receives.