The Tool Desk
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An AI agent that repeats tool calls or retries is usually following a feedback path that has no effective exit—not necessarily stuck because of one bad prompt. Trace the run, locate the repeated path, then add a stopping rule or recovery branch that applies to that path. A turn or step limit can contain a runaway run, but it does not explain why the cycle happened.
What counts as an agent loop?
An agent run is a control loop: the model produces an answer or requests a tool, the application executes that tool or a handoff, and the result is fed into the next model call. The run ends when the agent produces a final response or a configured stopping condition is reached. OpenAI describes its Agents SDK runner as looping until it reaches a real stopping point in its Running agents documentation.
Iteration is not automatically a bug. An agent may need multiple observations, reasoning steps, tool calls, and state updates to complete a task. The concern is an unbounded feedback path that continues costly or state-changing work without making useful progress.
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How to diagnose a run that keeps repeating
- Reproduce one failing run with tracing enabled. Capture the active agent or workflow node and the sequence of model generations and tool spans. OpenAI’s Tracing guide describes recording the execution of agent runs.
- Find the repeated segment. Compare tool names and arguments, results and errors, handoffs, and workflow transitions. Exact repetition is one signal; a changing sequence can still cycle through the same nodes or accumulate state without progress.
- Follow the feedback edge. Ask what causes the next invocation: a tool result, an error, retry, repair step, workflow transition, or delegated response. The cycle may be created by framework behavior rather than a visible
whileloop in your application code. - Check the stop behavior. Look for a missing exit condition, forced tool selection, an absent or overly broad execution limit, and a limit that governs the wrong unit—for example, model turns rather than retries or graph steps.
- Add a bound and a meaningful exit or recovery branch. Stop when a useful condition is met, route repeated failures to recovery, or return a tool result directly when no further model decision is needed. Preserve the trace so you can identify why the boundary fired.
What to compare in the trace
A useful trace makes it possible to reconstruct both the repeated action and the reason it was repeated. Compare:
- Generation sequence and the agent or node responsible for each call.
- Tool names, arguments, recorded inputs and outputs, and whether the result changes.
- Error messages and status, including whether the same failure is passed back into the next step.
- Handoffs and workflow transitions, including paths that return to an earlier node.
- Duration and state or context growth across repeated calls.
- Parent and child execution relationships when delegation is involved.
If each attempt has a different input and produces a useful new result, the run may be legitimate iteration. If the same error or unchanged observation is repeatedly reintroduced, inspect the retry or repair path before changing the prompt. If calls differ but keep traversing the same workflow cycle, inspect the transition conditions and the state they depend on.
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Choose a limit that covers the feedback path
A limit only protects the unit it counts. A model-turn limit may not cap graph transitions or retries in the way you expect; a local tool-call cap may miss a cycle that passes through a handoff and returns through another node. Choose a boundary after mapping the full path, and decide what should happen when it is reached.
| Control | What it bounds | Boundary behavior or consideration |
|---|---|---|
OpenAI Agents SDK max_turns |
Agent turns in a Python SDK run | The SDK documents a turn limit; check the current version’s documentation for the exception and handling behavior. |
| LangGraph.js recursion limit | Graph execution steps | Use it to bound graph traversal; decide how the application should handle a limit being reached. |
| Direct-return tool behavior in LangGraph.js | Further model cycling after a tool result intended to conclude the run | Return the tool result directly instead of sending it back through another model cycle. |
| Application-specific retry or recovery condition | A particular retry, repair, or feedback path | Stop, return an error, or route to recovery when the condition is met; ensure the condition also covers indirect re-entry. |
These controls are not interchangeable. OpenAI’s Python SDK documentation covers max_turns in Running agents. LangGraph.js documents recursion limits, direct-return tools, and the risk that forcing tool use without a stopping condition can create infinite loops in its Tools documentation. Verify current option names and behavior against the version you deploy.
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What a published study does—and does not—show
A 2026 preprint by the IAL-Scan authors reports 68 confirmed infinite agentic-loop failures across 47 projects, after manual review of 74 potential findings in 6,549 LLM-agent repositories. The authors report 91.9% precision for the tool’s findings. These are static-analysis results, not an estimate of how often deployed agents loop. The paper describes cycles involving explicit loops, recursive calls, workflow transitions, retries or repair, tool re-entry, and multi-agent delegation. Read When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents for the study’s scope and methods.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.After a limit fires
Treat the limit as containment, not a diagnosis. Use the trace to identify which path reached the boundary, what repeated, and whether the next action should stop, return a tool result, or enter recovery. If the run grew in context or repeated a side-effecting action, include that consequence in your incident review as well as the call count.
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