Agentic Loop
The core execution cycle of an AI agent: observe the current state, reason about what to do next, take an action (call a tool, generate a response, update memory), observe the result, and repeat until the goal is achieved or a stopping condition is met. The agentic loop is what distinguishes agents from single-shot LLM calls—agents iterate, adapt to intermediate results, recover from errors, and pursue multi-step objectives. Loop control (when to continue, when to stop, when to ask for human input) is one of the hardest design problems in agent engineering.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
A coding agent receives a bug report. Loop iteration 1: read the error log. Iteration 2: find the relevant source file. Iteration 3: identify the bug. Iteration 4: write a fix. Iteration 5: run tests. Iteration 6: tests fail—revise the fix. Iteration 7: tests pass—submit the PR. Each iteration observes, reasons, and acts.
Frequently asked questions
- How do you prevent an agentic loop from running forever?
- Common safeguards: maximum iteration limits (e.g., 25 steps), token budget caps, timeout limits, repeated-action detection (agent stuck doing the same thing), and confidence thresholds (if the agent can't make progress, escalate to a human). The best agent frameworks combine multiple safeguards so no single failure mode causes runaway execution.