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An AI workflow that seems to run forever usually has a control-flow problem: it has no effective way to recognize that the task is complete, or the condition it needs to stop is unreachable. Saving state so a run can pause and resume helps it survive interruptions, but persistence alone does not tell it when to stop.
Why does my AI agent keep looping?
An agent run commonly works as a cycle: the model decides what to do, it may request a tool, the tool runs, and control returns to the model. That cycle continues until the runner reaches a stopping point. In OpenAI’s Agents SDK documentation, a final answer with no further tool work is one such stopping point. OpenAI’s running-agents guide describes this loop and its limits.
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The cycle can continue indefinitely when the workflow has no valid completion condition, when its condition cannot be reached, or when a subagent fails to produce the state needed to satisfy it. Google Cloud warns that these failures can leave a loop running indefinitely in its agentic AI design-pattern guidance.
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- The run loop: the repeated model, tool, and handoff steps within an execution.
- Persistence: saved state that lets work continue after a pause or interruption.
- Long-running orchestration: infrastructure that can wait for events and resume work without keeping one process open.
Persistence and orchestration address continuity. Neither supplies a missing definition of “done.”
What should count as done?
Define completion in terms of a result someone can verify, not simply the model saying it is finished. Depending on the task, the result might be a saved file, a confirmed record update, a completed review, or a verified answer with required evidence. The exact test should match the work.
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- Name the expected outcome: specify the artifact, state change, or verified result the workflow must produce.
- Check the actual state: evaluate whether that result exists or the required conditions are met, rather than relying only on a model-generated claim.
- Define what happens when the test fails: allow a bounded retry, return a partial result, or ask for human help. Avoid an implicit instruction to keep trying forever.
A stopping test can be correct in principle and still fail in practice if the workflow never updates the state the test reads. Trace the path from the tool’s result to the condition that decides whether the loop continues.
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How do I make an AI workflow stop safely?
Use a completion test and independent execution limits together. A limit prevents unbounded work; it does not prove the task succeeded. OpenAI’s Agents SDK documents a max_turns limit and raises MaxTurnsExceeded when a run exceeds it. Treat that as a guardrail, and make the resulting limit failure useful to the person or system handling the task.
- Set a turn budget. Choose a maximum number of model turns appropriate to the task. Handle limit exhaustion explicitly rather than allowing it to appear as a successful completion.
- Bound retries and elapsed time. Add retry and time limits where relevant; for workloads with a meaningful spending cap, set that too. Use limits suited to the task rather than assuming one bound covers every failure.
- Detect no progress. If repeated steps leave the relevant state unchanged, stop, report the blockage, or request help instead of repeating the same action. This is a design safeguard against a loop that cannot reach its stopping condition.
- Make retries safe. Before repeating an external action, check whether it has already happened or design the operation to be idempotent, so a retry does not duplicate an effect.
- Record why the run ended. Log state changes, tool calls, handoffs, retries, and the stop reason. That makes it easier to distinguish slow progress from a loop or a limit being reached.
A 2026 arXiv preprint, “When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents”, describes unbounded feedback paths that can repeat calls, transitions, or handoffs. It identifies possible harms such as cost exhaustion, denial of service, growing context, and repeated side effects. These are reasons to bound and observe a workflow, not evidence that every agent run will exhibit them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should a workflow pause for approval?
Not every action should be handled like another reversible internal step. If a decision requires human judgment or final authorization—especially before an external or sensitive action—pause the workflow for approval. Google Cloud’s design-pattern guidance discusses human-in-the-loop patterns; OpenAI’s Agents SDK documentation covers durable execution integrations for workflows that need to continue later.
For an approval pause to work, save enough run state to resume the same work once approval arrives. Make the approval decision an explicit input to the next step, and check before resuming whether the intended action has already occurred. A pause is not a successful completion unless the task’s defined completion test is satisfied.
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For work that spans long waits, do not keep a process open just to preserve continuity. Use persistent state and resume when an event arrives, or use durable orchestration suited to the application. OpenAI’s Agents SDK documentation discusses sessions and durable execution integrations. Cloudflare’s long-running agents guide describes persistent state, hibernation, and event-triggered wakeups.
Choose an approach based on the workflow’s actual needs: whether state must survive a restart, which execution limits apply, how approval pauses resume, whether external actions are safe to retry, and whether maintainers can see the trace and stop reason. Durable state is useful for continuity; a separately defined completion test and bounds still govern whether the work ends.
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