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Set a finite limit on the agent’s turns or tool iterations, then inspect the run history to find what kept it going. A limit creates a stopping boundary; by itself, it does not detect duplicate actions or explain why the run exceeded the bound.
Why an AI agent can keep running
An agent typically works in a loop: the model responds, the runtime may execute a tool call or hand control to another agent, and the model is called again. The loop ends when it reaches a stopping condition, such as a final answer or a configured limit. OpenAI describes this sequence in its Running agents guide.
A long run is not necessarily a loop bug: a complex task may need many steps. But repeated tool calls, a tool that fails to make progress, or a handoff cycle can also keep the process from reaching a final answer. A hard limit bounds execution without distinguishing among those causes.
Choose a runtime limit
The two documented controls below count different things, so choose the one that matches how your framework defines a step. Neither is documented as a general-purpose detector of repeated or unproductive behavior.
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| Framework | What to configure | What happens at the bound | Documentation |
|---|---|---|---|
| OpenAI Agents SDK | Maximum turns for a run (`max_turns`) | The runner raises `MaxTurnsExceeded` when the maximum is exceeded. The reference notes that `max_turns=None` disables the turn limit. | OpenAI: Running agents |
| Microsoft AutoGen AgentChat | `max_tool_iterations`, the maximum number of tool iterations | The configured iteration bound stops further tool iterations. The guide’s example comment describes its own setting as “At most 10 iterations of tool calls before stopping the loop”; that example is not a universal recommended limit. | AutoGen: Agents |
Set a bound that fits the task
Choose a finite maximum based on the expected work and the cost or risk of allowing another step. There is no universal number established by these framework references. For unattended runs, avoid disabling the bound: an unbounded run can continue without this particular stopping condition.
Diagnose a run that hits its limit
Reaching the cap tells you the run exceeded its configured bound; it does not tell you whether the task was legitimately long, a tool failed, the same action repeated, or the orchestration handed control around in a cycle. Use the run history to locate the pattern rather than treating the limit error as the diagnosis.
- Find where execution stopped. Check whether the runtime ended at the configured turn or tool-iteration limit, and whether it produced a final answer.
- Trace the sequence. Review model outputs, tool calls and their results, and any handoffs. These are the stages in the documented agent loop.
- Look for lack of progress. Compare repeated calls and their inputs and results. If the same action recurs without advancing the task, identify whether the cause is an unchanged model decision, a tool error, or orchestration behavior.
- Change the relevant control. Keep the hard cap. If a repeated pattern is the problem, add workflow-specific checks—for example, detect repeated tool inputs or require evidence of progress before allowing another iteration.
Prevent repeated actions safely
A turn or iteration cap is a last boundary, not a substitute for controls tailored to the task. Application-level checks can stop a known repeated pattern earlier, but what counts as a duplicate or meaningful progress depends on the workflow; the cited documentation does not establish one universal detector.
- Define which actions are safe to retry and which require a fresh validation or explicit approval.
- For consequential tool actions, enforce suitable application permissions and validate inputs and results before allowing the next step.
- When a run stops at its cap, preserve the run history so a person or recovery process can decide whether to resume, revise the task, or stop.
Check the documentation for the framework version you have deployed before changing configuration: parameter names and behavior can vary by version.
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