Start by checking what the agent actually did, not just its final answer. A trace or run log can show whether it is repeating actions, waiting for approval, failing in a tool or API, or returning a wrong result despite a successful run. Find the earliest step that diverged from the expected behavior, fix that layer, and replay the case.
First determine what “stuck” means
An agent run can look stalled for several different reasons, and each calls for a different response. Check the run or session status before changing the prompt.
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- Still running: The agent may be repeating a tool call, cycling through handoffs, or taking time in a slow tool.
- Failed or stopped: Look for a run limit, validation or guardrail exception, tool error, or API error.
- Paused: The agent may be waiting for a required human approval. A pause is not necessarily a failure.
- Completed with a wrong answer: The run reached an endpoint, but a model decision, tool result, supplied context, or final explanation may be incorrect.
- Only the interface appears stuck: A spinner or blank page can be a browser, network, or product issue rather than an agent-logic failure.
For example, the OpenAI Agents SDK describes a run as a loop: the model responds, the runtime executes requested tools or follows a handoff, and the run stops when the model returns a final answer without further tool work. Limits and runtime errors are different from approval pauses. See the OpenAI guide to running agents for the behavior of that SDK; other frameworks may differ.
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Preserve a reproducible failure
Before editing prompts or configuration, save enough information to replay the failing case. Otherwise, a change may appear to help simply because the original conditions are no longer present.
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- The exact user input and relevant conversation or session state.
- The agent configuration, tool definitions, permissions, and routing or handoff setup.
- The model and version, if available, plus the time of the run.
- What the agent was expected to do and what it actually did.
- A known-good run, if the problem occurs only for one user, account, or environment.
For a runaway run, stop it or apply the framework’s run or turn limit while keeping its trace or logs. The exact loop-protection mechanism depends on the framework.
Read the run from its first step forward
Inspect the timeline in order and find the earliest suspicious event. A final answer rarely identifies the root cause by itself. If the answer contains a wrong value, follow that value backward to the first model output or tool result that introduced it.
When available, inspect each span’s model input and output, tool name and arguments, tool result, status, duration, and error details. OpenAI’s tracing guide describes these details and dashboard inspection and trace export. A trace records activity; it does not prove that the model’s interpretation or a tool’s result is semantically correct. Compare the recorded steps with the task requirements and the actual state of the system the tool accessed.
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Diagnose the first failing layer
Repeated actions or no progress
Look for the same tool call with unchanged arguments or results, repeated handoffs between agents, or successive steps that leave the relevant state untouched. Then check whether the instructions give the agent a clear stopping condition and whether the runtime has a turn or run limit. A repeated call can indicate a weak decision or termination rule, but it can also be a symptom of a tool that returns an error or unchanged state; inspect the result before changing the prompt.
Tool call or protocol failure
Check the tool’s name, argument schema, permissions, result format, and how the result is passed back to the model. Tool failures are not always caused by the model selecting the wrong action: the orchestration protocol and schema can also be at fault.
For example, Anthropic’s tool-use documentation describes API-specific requirements and failure cases: a tool_use needs a corresponding tool_result in the required position; deferred tool loading requires at least one tool to be immediately available; and strict tool schemas support only a limited set of regular-expression patterns. These details apply to Anthropic’s API, not automatically to other frameworks. Keep tool outputs focused on data rather than mixing in developer instructions.
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API, request, or runtime error
Inspect the HTTP response and structured error fields, then check the relevant session, turn, or environment status. The OpenAI Agents API reference distinguishes issues such as invalid input or configuration, authentication or access, missing resources, state conflicts, executor compatibility, MCP startup failures, and temporary service errors. Correct deterministic request or configuration problems before retrying. For a transient service problem, check that work is saved and follow the service’s retry guidance.
Wrong answer without an exception
Compare the decision and arguments the model produced with the tool result it received, the context supplied to it, and the facts expected in the final answer. Identify the first step that became wrong: for example, an unsuitable tool choice, an incorrect argument, an inaccurate tool result, missing context, or a faulty explanation of correct data. Turn the observed failure into a checkable evaluation criterion rather than relying on whether the answer merely sounds plausible.
ChatGPT spinner or blank page
If the problem is with ChatGPT’s web or app interface rather than an agent you operate, use the product-level troubleshooting path. The OpenAI Help Center troubleshooting guide recommends checking service status, restarting or starting a new chat, testing another browser or network or a private window, disabling extensions, VPNs, or security filters, and collecting diagnostic logs if the issue persists. An interface spinner alone does not establish that an agent’s logic is looping.
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Make one targeted change, then verify it
- State the failure precisely. Describe the first incorrect or stalled step and the outcome you expected.
- Change the layer that caused it. Adjust the prompt for an instruction or decision problem; change the tool definition, schema, permissions, or result handling for a tool or protocol problem; change routing or termination rules for a looping workflow; or address the API or service issue indicated by the error.
- Replay the saved case. Check whether the original input now reaches the expected result and whether the trace shows that the faulty step changed.
- Test representative cases. Include nearby successful cases and cases likely to trigger the same failure so a fix does not simply shift the problem.
- Keep repeatable checks. For ongoing development, retain a small evaluation dataset and compare prompt, tool, routing, and guardrail changes with graders or equivalent criteria.
OpenAI’s guide to evaluating agent workflows recommends using representative traces to debug active failures, then datasets, graders, and evaluation runs to compare changes and monitor quality over time. It defines a trace as “the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run.”
Choose observability that answers the questions you have
When evaluating a tracing or debugging setup, check whether it records the full run—not just the final model response—and whether it captures useful inputs, outputs, tool and handoff steps, status, duration, and errors. Also consider framework and runtime support, trace export or session correlation, data redaction and retention, access controls, and whether the system supports repeatable evaluations and regression checks.
Logging can expose sensitive prompts, tool arguments, and results. Confirm how data is redacted, retained, exported, and access-controlled before enabling detailed traces in a production environment. OpenAI’s Agents SDK troubleshooting guide says model and tool data remains redacted by default in debug logs. Check the documentation for your specific framework and version before assuming the same defaults apply.
Quick Recap
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