Loops for Agents: Letting AI Iterate Autonomously

What is a loop in an agent context?

A loop means an agent doesn't just respond once — it works repeatedly and independently: complete a task, check the result, move to the next task, without you stepping in after every single round. This is the "agent loop" (think → act → check), just repeated over many cycles, often over an extended period of time.

What makes a loop good

• A clear goal: the agent needs to know when it's "done"
• Stop conditions: a maximum number of rounds, a time limit, or a budget cap
• Checkpoints: tests, validations, or human review points instead of blindly continuing
• Failure detection: the loop needs to notice when it's going in circles, instead of endlessly retrying the same wrong fix

An example setup

A loop that works through a list of open bugs: pick a bug → find the cause → write a fix → run tests → if green: next bug, if red: retry (max 3 attempts) → if uncertain: ask a human instead of guessing.

The biggest risk

Without guardrails, an autonomous loop can repeat expensive mistakes, get stuck heading in the wrong direction, or quietly burn through API budget. That's why every production loop needs: a budget, a time frame, clear success criteria, and points where a human can step in.