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To debug an AI agent, inspect a complete failing run and find the first point where its behavior diverges from what you expected. A tool call is a chain of events—not a single model action—so check whether the model requested the right operation, your application executed it, and the result returned to the model. For loops, follow every request-and-result cycle and the runner’s stopping behavior. For hallucinations, compare the specific claim with the evidence available at generation time. Then preserve the failure as a repeatable evaluation so you can tell whether a fix works without breaking other cases.
What should I inspect before changing the prompt?
Start with the run, not a guessed cause. A prompt change may help when the model makes the wrong decision, but it will not fix a tool callback that never executes, a result that never reaches the model, or a runner that keeps going after it should stop.
Capture the input that triggered the problem, the configuration and version used, and the end-to-end execution. OpenAI’s workflow-evaluation guidance recommends beginning with traces when debugging agent behavior, including checking whether the agent chose the right tool or handed work off when expected. A trace is useful because it can show the sequence of events rather than only the final response.
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- Reproduce the failure. Use the same user input and the same relevant configuration. Record the run so you can compare it with later attempts.
- Open the full session and turn. Follow the execution from the initial request through each model generation, tool operation, handoff, and final response.
- Find the earliest divergence. Identify the first recorded event that differs from the expected path. Do not begin with the last visible symptom if an earlier decision or missing result explains it.
- Inspect the event details. Depending on the tracing surface, spans can include recorded inputs and outputs, duration, status, and—for tool spans—request arguments and results when available. OpenAI’s tracing guidance describes this session, turn, and span view.
Keep the failing input and trace together. A final answer such as “I could not find that information” cannot, by itself, tell you whether retrieval failed, a tool was never requested, or a useful result was ignored. The timeline is what lets you distinguish those paths.
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How does an AI agent tool call work?
A tool call has separate model and application stages. The model requests an operation using the tool interface you exposed; application code or a server-side tool performs it; then the result must be returned to the model if it is to use that result in its next response.
Anthropic’s Claude Platform documentation describes tools as a contract: the application defines the operations and their input shapes, and the model emits a structured request. Its concise formulation is, “The model never runs anything on its own.” That distinction is crucial when diagnosing a missing action: a model request in a trace does not prove that your code executed the operation.
| Stage | What to verify | What a failure points toward |
|---|---|---|
| Tool availability | The intended operation is exposed to the model with the expected name and input shape. | The tool contract or the configuration supplied to the model. |
| Model request | The generation requests the appropriate operation and supplies suitable arguments. | Tool selection, instructions, available context, or argument generation. |
| Application execution | The runtime receives the request and invokes the intended operation. | The callback, runtime, routing, or execution boundary. |
| Result return | The operation’s result is formatted and included in the next conversation request. | Result handling or conversation assembly. |
| Continuation and stop | The runner continues when work remains and stops at the intended terminal outcome. | Orchestration and control flow. |
For client-executed Claude tools, the documented cycle is: the assistant requests a tool, the application executes it, the application returns a tool_result, and the conversation continues while the model requests more tools. OpenAI’s Agents SDK describes a related run loop that can include model calls, tool calls, and handoffs until a final answer or another stopping point. Exact event names and mechanics differ by framework, so inspect the documentation for the framework and version you actually use rather than assuming these labels apply everywhere.
Why isn’t my AI agent calling tools?
First determine whether the model did not request a tool or the application failed to carry out a request. Those are different failures and call for different fixes.
If there is no tool request in the trace
Inspect what tools and input shapes were available to the model in that generation, along with the recorded request and conversation context. Ask whether the relevant operation was actually exposed, whether its purpose and inputs were clear, and whether the model had enough information to choose it. Compare this run with one in which the agent correctly made the same kind of decision, if you have one.
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Do not assume that adding more forceful wording is the right fix. The first question is whether the model had a usable tool contract and the information needed to select it. If the trace shows that the model generated a normal response instead of a tool request, the failure is at or before the model-decision stage; changing the application’s tool executor alone would not address that observed divergence.
If the trace shows a tool request but no successful result
Follow the request across the application boundary. Confirm that your runtime received the requested operation and arguments, and look for a corresponding execution result or error. A request without a corresponding successful result shifts attention toward the callback or runtime path rather than proving that the model failed to call the tool.
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If the tool returned something but the answer is still wrong
Compare the returned value with the next model request. Check whether the result was empty, incomplete, stale, or unrelated to the question, and whether it was actually included in context. This separates a poor tool result from a result that was good but lost or misrepresented on the way back to the model.
Framework-specific error names, retry behavior, iteration limits, and tracing details vary. Do not infer a particular default or error state from general agent mechanics; verify it in the documentation for the exact runtime version and inspect its recorded events.
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Why does my AI agent keep looping?
Read a repeated run as a timeline and compare each request with the result that followed it. A loop can arise because the model keeps asking for the same operation, because the application repeats work, because useful results are not returned to the model, or because control flow does not recognize a terminal outcome.
For each cycle, note the requested tool and arguments, the execution status and result, the next request sent to the model, and whether the runner continued or stopped. Use the pattern—not just the number of turns—to locate the first repetition that should not have happened.
| Trace pattern | What to examine next |
|---|---|
| The same tool and arguments recur, and each result is returned. | Inspect why the next model generation asks for the same action despite the available result. Compare the result and conversation context with what the task requires. |
| The same operation appears repeatedly, but application execution or result status is missing or unsuccessful. | Follow the request through the runtime and inspect how errors or missing results are handled. |
| A useful result appears in an application log but not in the next model request. | Inspect result formatting and conversation assembly; the model cannot use information it did not receive. |
| The task appears complete, but additional calls or handoffs continue. | Inspect how the runner identifies terminal outcomes and how its control flow treats them. |
| Different tools or arguments recur without making progress. | Check the evidence and context available at each generation, along with the execution results. The changing requests may point to an information problem rather than a repeated identical call. |
These patterns are diagnostic hypotheses, not a universal list of causes. OpenAI’s Agents SDK documentation describes a run as continuing through model calls, tool calls, and handoffs until a final answer or another stopping point; Anthropic’s tool-use documentation describes the request-and-result cycle. The trace for your own application is needed to establish which part is failing.
When investigating, distinguish “the model asked again” from “the application ran the same request again.” The former should be visible in successive model generations; the latter belongs to execution or orchestration. If the trace does not expose enough information to make that distinction, improve observability before changing behavior based on guesswork.
How do I stop an AI agent from hallucinating?
Start with the exact unsupported statement and work backward to the evidence available when the model generated it. A confident answer is not proof that the claim was supported.
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- Quote the claim precisely in your own debugging record. Separate the unsupported factual statement from surrounding wording so you can test what evidence would substantiate it.
- Inspect the generation’s available context. Check whether relevant retrieval, search, or tool output was produced and whether it appeared in the request sent to the model.
- Check the evidence itself. Determine whether the returned information was relevant and current enough for the claim. If it was unavailable, stale, or incomplete, locate the break in retrieval or tool execution.
- Verify important claims independently. OpenAI Help Center guidance on whether ChatGPT tells the truth describes hallucinations as factually inaccurate output, including fabricated quotes, studies, citations, or references, and advises verifying important information. It also cautions that a model can sound confident while wrong.
- Classify the failure before changing instructions. The evidence may have been unavailable, poorly retrieved, missing from context, or insufficient for the answer the model gave. A trace can narrow down where that evidence chain broke, but reproducing the particular system is necessary to establish its root cause.
Do not label every incorrect answer a prompt failure. A prompt cannot supply evidence that the application never retrieved or passed along. Conversely, a trace that shows relevant evidence in context does not prove that the generated claim follows from it; that is a model-response failure to evaluate against the evidence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I turn a failure into a repeatable evaluation?
Save representative failures as test cases with a defined input, an expected task outcome, and checks for the behavior that matters. OpenAI recommends moving from individual traces used for debugging to datasets and evaluation runs when repeatability and comparison are needed.
For agents, evaluate the trajectory as well as the final answer. Anthropic’s January 9, 2026 article “Demystifying evals for AI agents” describes evaluation as providing an input and applying grading logic, and notes that multi-turn actions can modify state and let errors propagate. A final-answer-only check can miss an unnecessary tool call, incorrect arguments, a lost result, or an agent that did not stop appropriately.
What to include in an agent test case
- Input and setup: the user request and the configuration or version needed to reproduce the relevant behavior.
- Task outcome: what counts as a correct result for the task, stated so a grader or reviewer can check it.
- Trajectory checks: whether the appropriate tool was requested, the arguments were suitable, execution produced a usable result, and that result was handled correctly.
- Stopping behavior: whether the run ended at an acceptable outcome instead of stopping prematurely or continuing without purpose.
- Evidence checks: whether important factual claims in the response are supported by information available to the agent.
Run the same cases before and after a change, and include cases that previously worked. Agent outputs can vary, so a single successful attempt does not establish that the failure is fixed. Comparing cases helps reveal whether a local correction has introduced a regression elsewhere.
Keep each evaluation tied to the failure mode it is meant to catch. A case for a missing tool request should check the relevant request behavior; a case for a fabricated claim should check evidence support. This makes an evaluation easier to interpret than a single broad pass/fail score that does not show what went wrong.
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What should an agent trace let me see?
A useful trace lets you reconstruct the session and locate the transition where behavior changed. Before relying on a tracing surface, check what it records, what can be retained or exported, and whether its data handling fits your application.
- Model visibility: recorded inputs and outputs for relevant generations, so you can determine what the model was asked and what it returned.
- Tool visibility: tool names, request arguments, results, statuses, errors, and timing where the tracing surface makes them available.
- Workflow visibility: session, turn, span, and handoff relationships that let you follow the end-to-end run.
- Evaluation workflow: a practical way to use representative traces to build repeatable datasets or evaluation runs.
- Data handling: current retention and export behavior, plus privacy constraints relevant to the information in your traces.
OpenAI’s tracing guidance describes session, turn, and span inspection, including recorded inputs and outputs, duration, status, tool request details, results when available, and export behavior. Actual product availability, export capability, and retention settings can change; verify them in the current documentation for the specific service you use. The general trace structure is a debugging aid, not a guarantee that every provider records every field.
Frequently Asked Questions
How many times should I rerun an agent before deciding a fix worked?
There is no universal run count established for every agent or task. Compare repeated runs on the same representative cases, including previously successful ones, and use evaluation results appropriate to the variability and risk of your system rather than treating one pass as proof.
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Check the trace for user data, credentials, private tool results, or other sensitive content before sharing it. Also review the tracing service’s current retention and export behavior; those details depend on the provider and configuration.
Can I diagnose an agent failure from the final answer alone?
Usually not with confidence: the same answer can result from a missing tool request, a failed execution, a lost result, or an unsupported generation. A reproducible run with the relevant intermediate events is needed to distinguish those paths.
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