To diagnose an AI agent that keeps repeating itself, trace one run from model calls through tools, retries, workflow transitions, and handoffs. Find the feedback path that sends the same work around again, then add a stop condition or recovery branch that covers that path. A turn or step limit can contain a runaway run, but the trace is what explains why it happened.
What counts as a loop—and what is normal iteration?
An agent run is a control loop: the model produces an output, the application executes any requested tools or handoffs, and their results become input to later steps. The run ends when the agent produces a final response or reaches a configured stopping condition. OpenAI describes this cycle in its Agents API guide and its account of how the Codex agent loop works.
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Repeated calls are not automatically a defect. An agent may need several rounds of perception, reasoning, tool use, and state updates to complete a task. The problem is an unbounded feedback path: the same work, or a cycle of work, keeps invoking costly operations or growing state without making useful progress.
The cycle may not appear as an obvious while loop in your application. It can pass through retry or repair logic, workflow transitions, tool dispatch and re-entry, recursive calls, or delegation to another agent. A prompt-only review can miss these paths.
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How to inspect a failing run
1. Capture a trace
Reproduce one failing run with tracing enabled. Retain the sequence of model generations and tool spans, including which agent or workflow node was active. OpenAI’s tracing guide describes tracing for agent executions.
Useful evidence includes tool names and arguments, recorded inputs and outputs, status, errors, duration, and parent-child execution relationships. This lets you compare what happened, not just what the final answer looked like.
2. Find the repeated segment
Compare calls and transitions across the trace. Check whether the same tool is invoked with the same arguments, whether it returns the same result or error, and whether the run repeatedly visits the same nodes or hands work to the same agent.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsExact duplication is only one signal. A sequence can change slightly while still cycling through the same workflow, or the run can keep adding tool output to its context without resolving the task. OpenAI notes that tool outputs are fed into later prompt input; many calls can exhaust the context window. Watch for rising context use and repeated side effects as well as repeated text.
3. Follow the feedback edge
Ask what caused the next execution of the repeated segment. Did a tool result prompt another call? Did an error trigger a retry or repair step? Did a workflow transition route back to an earlier node? Did a delegated agent return a result that caused the parent to repeat its request?
Tracing the cause-and-effect path distinguishes a model that is making legitimate progress from a framework or application that keeps reintroducing the same failure or unchanged observation.
4. Check the stopping behavior
Inspect exit conditions, retry limits, forced tool selection, and any turn or graph-step limits. Determine exactly what each limit counts: model turns, graph execution steps, retries, or a particular tool path. A local cap may not cover a cycle that travels through other workflow nodes or agent handoffs.
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Limits and stop behaviors serve different purposes. A limit contains execution when progress stalls; a meaningful exit or recovery branch defines what the application should do instead. OpenAI’s Python Agents SDK documents a max_turns limit. LangGraph.js documents recursion limits and tools that can return directly, avoiding another model cycle when the tool’s result should end the run.
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| Safeguard | What it bounds or changes | Boundary behavior |
|---|---|---|
OpenAI Python Agents SDK max_turns |
Agent turns in a run | The SDK documents an exception when the limit is exceeded. See Running agents. |
| LangGraph.js recursion limit | Graph execution steps | The graph stops at its recursion limit. See Tools. |
| LangGraph.js direct-return tool behavior | Further model execution after a tool result intended to conclude the run | The tool result returns without another model cycle. See Tools. |
These controls are not interchangeable: a turn cap does not necessarily bound every graph transition, retry, or delegated call. LangGraph.js also warns that forcing tool use without a stopping condition can create infinite loops. Verify the option names and behavior against the documentation for the version you use.
Make the boundary useful
When a bound fires, preserve the trace and make the outcome explicit. Depending on the application, stop and report that the run exceeded its limit, route to a recovery path, or return a tool result directly when no further reasoning is needed. Do not treat the limit itself as a diagnosis; record which condition fired so you can fix the underlying feedback path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why a runaway loop matters
Repeated execution can consume time and resources, repeat side effects, and grow the context with outputs that do not move the task forward. The 2026 IAL-Scan preprint reports 68 confirmed infinite agentic loop failures across 47 projects, identified after manual review of 74 potential findings in 6,549 LLM-agent repositories; the authors report 91.9% precision for the tool’s findings. This is a static-analysis study, not an estimate of how common loops are in deployed agents. See the preprint.
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Quick diagnostic checklist
- Capture a trace that shows generations, tools, inputs and outputs, errors, statuses, durations, and execution relationships.
- Compare repeated arguments, results, errors, node transitions, and handoffs.
- Trace the event that feeds the next execution, including retries, repairs, and delegation.
- Check whether limits cover the whole feedback path rather than only one local call type.
- Add a meaningful exit or recovery branch, then retain enough evidence to understand when it triggers.
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