To troubleshoot an AI agent, find the earliest step where its run diverges from the expected behavior—not just the mistake in its final answer. First determine whether the run is looping, paused, failed, waiting on a tool, or only appearing stuck in the interface. Then inspect the run trace, fix the failing layer, and replay the case alongside representative tests.
Start by preserving a reproducible failure
Before changing a prompt or configuration, save the failing case. Record the exact input, relevant conversation or session state, agent and tool configuration, model or version if available, timestamp, and expected outcome. If the issue affects only one user or environment, compare it with a known-good run.
This gives you a stable case to inspect and replay. Without it, a prompt change can make the symptom disappear while leaving the cause unclear.
Determine what “stuck” means
Check the run or session status before editing anything. A run that looks stalled may be doing one of several different things:
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- Looping: the agent keeps taking actions without reaching a final answer or making progress.
- Stopped by a limit or error: the runtime ended the run, for example because it reached a turn limit or encountered a guardrail or tool error.
- Paused for approval: the agent is waiting for a human decision. This is an intentional pause, not necessarily a failure.
- Waiting on an external tool: a service or integration may be slow, unavailable, or returning an error.
- Interface appears stuck: a spinner or blank page may indicate a ChatGPT web or app problem rather than a bug in your own agent.
An agent run commonly works as a loop: the model responds, the runtime handles any requested tools or handoffs, and the process stops when the model returns a final answer without further tool work. The OpenAI Agents documentation distinguishes this process from runtime and validation failures such as max-turn limits, guardrail exceptions, and tool errors.
Read the run trace from the beginning
Inspect the timeline from the first step forward and identify the earliest suspicious event. A final answer alone rarely reveals whether the problem began in the model’s decision, a tool call, the tool’s result, or later processing.
For that event, compare the model input and output, tool name and arguments, tool result, status, duration, and any recorded error. If the final answer contains an incorrect value, trace it backward to the earliest model or tool output that introduced it. OpenAI’s tracing guide describes spans that can capture this activity, including inputs, outputs, tool arguments and results, duration, status, and error details. Its agent evaluation guide describes a trace as the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run.
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A trace shows what was recorded, not whether each action was correct. Check each step against the task requirements and the actual state of the tool or system involved.
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Identify the first failing layer
Repeated actions or no progress
Look for the same tool call with unchanged arguments, repeated handoffs between agents, or actions that leave the relevant state unchanged. Check whether the routing logic can cycle and whether the agent has a clear condition for stopping. Review the configured run or turn limit as well; a limit can end a run, but does not by itself explain why the agent failed to make progress. If a run is consuming resources without advancing, stop or cap it while preserving the trace.
Exact loop-prevention controls vary by framework, so use the runtime’s documentation for the version you are running. Do not treat every repeated call as a loop: an agent may legitimately retry or query a tool more than once. The key question is whether the repeated action changes the state or advances the task.
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Tool definition, protocol, or result problems
Inspect the tool’s definition, argument schema, permissions, result shape, and the way its response is returned to the model. A model can choose a reasonable action and still fail because the orchestration protocol or schema is wrong.
For example, Anthropic’s tool-use documentation specifies that each tool_use must have a corresponding tool_result in the required position. It also documents constraints involving deferred tool loading and supported regular-expression patterns in strict tool schemas. Check the current documentation for the API and version you use. Keep tool output focused on returned data rather than mixing it with developer instructions.
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Inspect the HTTP response and structured error fields, then check the run, session, and environment status. OpenAI’s error-code documentation covers different categories, including invalid input or configuration, authentication and access problems, missing resources, state conflicts, executor compatibility, MCP startup failures, and temporary service errors.
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Correct deterministic request or configuration problems before retrying. For a transient service issue, check that work was saved and follow the service’s retry guidance rather than repeatedly resubmitting without examining the error.
Wrong answer without an exception
Compare the agent’s selected tool and arguments, the tool result, the context supplied to the model, and the final response with the facts the task requires. Find the first step that introduced the wrong information. If the trace does not make the failure easy to spot, turn the expected behavior into an explicit evaluation criterion—for example, whether the agent selected the required source or returned a value present in the tool result.
Tracing helps locate workflow problems; evaluation checks whether the workflow meets its requirements across cases. OpenAI’s workflow evaluation guide recommends starting with traces for active debugging and using graders, datasets, and evaluation runs for repeatable comparisons.
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ChatGPT spinner, blank page, or unresponsive interface
If the problem is in ChatGPT’s web or app interface rather than an agent you operate, use the product’s support steps. 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. A product-level spinner is not, by itself, evidence of an agent logic bug.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Fix one cause, then check that the fix generalizes
- Choose a targeted change. Adjust the layer implicated by the first failure: the prompt or context, tool definition or result handling, routing, runtime configuration, or guardrail.
- Replay the original case. Check that the same input now reaches the expected outcome and inspect the new trace for unintended changes.
- Run representative cases. Include successful cases as well as similar failures, so a fix does not merely mask one example or break adjacent behavior.
- Keep repeatable checks. Maintain a dataset of representative cases and use graders or equivalent checks to compare changes to prompts, tools, routing, and guardrails over time.
For choosing a tracing or observability approach, compare whether it records the full run—including model, tool, and handoff steps—captures useful inputs, outputs, statuses, durations, and errors, supports your framework and runtime, allows trace export or session correlation, and provides suitable redaction, retention, and access controls. Also check whether it supports repeatable evaluations and regression checks. OpenAI’s tracing guide documents dashboard inspection and trace export; its SDK troubleshooting guide says model and tool data remain redacted by default in debug logs. Confirm the relevant behavior for the SDK version you use.
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