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Debug an unreliable AI agent by finding the first point where its observed behavior diverges from the behavior you expected. Start with a complete trace of one failing run—not a new prompt. Follow every model generation, tool request, application callback, tool result, and stop decision. A tool call has two distinct halves: the model proposes a structured operation, and your application or server executes it and returns the result to the conversation. Loops usually come from broken control flow or unusable results; hallucinations usually mean the answer exceeded the evidence available at generation time. Once you isolate the break, turn that failure into a repeatable multi-turn evaluation so a fix can be compared against both failing and previously successful cases.
What should I inspect first when an AI agent fails?
Reproduce one failure end to end and inspect the earliest incorrect transition. The final answer is often only a symptom: a missing tool result, an incorrect argument, or a runner that continued after a terminal error may have happened several turns earlier.
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- Capture the exact input and configuration. Save the user message, system and developer instructions, model identifier, tool definitions, retrieval settings, feature flags, and application version used for the run. A prompt that works in a test console may fail after a configuration or schema change.
- Preserve the complete session. Record the session, each turn, every model output, tool request, tool response, handoff, error, retry, and termination decision. Include timestamps and correlation IDs so a request can be followed across services.
- Reconstruct the timeline. Read events in order, not as isolated log lines. Mark the first place where the actual event differs from the expected one—for example, a tool was available but not selected, a callback never ran, or a valid result was not included in the next model request.
- Separate symptom from boundary. A confident but unsupported sentence is a response-quality symptom; the root cause may be absent retrieval evidence. A repeated tool request may be model behavior, an application retry, or a result that never reached the model.
OpenAI’s workflow-evaluation guidance recommends starting with traces when debugging agent behavior, including whether the correct tool was selected and whether a handoff occurred when expected. The tracing guidance describes sessions, turns, and spans that expose recorded inputs and outputs, duration, status, and—when available for tool spans—the request arguments and result.
How does an AI agent tool call actually work?
The model does not execute your function merely because a tool is listed. It emits a structured request; application code or a server-side tool performs the operation; the resulting message must be sent back as part of the next conversation request.
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1. The model chooses an operation
Your application supplies tool names, descriptions, and an input schema. The model decides whether a tool is appropriate and emits a tool request with arguments that should conform to that schema. This is a decision, not execution. Anthropic’s official Claude Platform documentation states: “The model never runs anything on its own.”
2. The application executes the request
The runtime receives the structured request, validates arguments, authorizes the operation, and calls the implementation or remote service. A request visible in a trace without a corresponding execution span points to the callback, routing, permissions, or runtime boundary.
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3. The result returns to the model
After execution, the application adds a tool-result message to the conversation and asks the model what to do next. For client-executed Claude tools, the documented cycle is an assistant tool request, application execution, a returned tool_result, and another model turn while tool work continues. If the result is omitted, attached to the wrong call, or formatted so the model cannot use it, the next decision is made as if the operation never completed.
4. The runner decides whether to continue
An agent runner repeatedly handles model calls, tool calls, and handoffs until it reaches a final answer or another stopping point. OpenAI’s Agents SDK describes this as a run loop. Your orchestration layer must continue when work remains and stop on a terminal outcome, an unrecoverable error, or a deliberately enforced limit.
How can I locate the first bad transition?
Classify the first divergence into one of five boundaries. This prevents changing a prompt to fix a runtime defect or changing a tool schema to fix a stop-condition bug.
| Boundary | Questions to answer in the trace | Typical evidence |
|---|---|---|
| Model decision | Did the model request a tool, choose the right one, and provide usable arguments? | The recorded generation, selected tool name, and parsed arguments |
| Tool contract | Does the exposed operation describe the intended input shape and required fields? | Tool definition, schema validation output, and argument errors |
| Application execution | Did the application receive and execute the request? | Callback or server span, authorization result, downstream request, and exception |
| Result handling | Was the result returned in the next conversation request in a usable form? | Tool-result message, call identifier, serialized payload, and next model input |
| Control flow | Did the runner continue while work remained and stop at a terminal outcome? | Iteration decisions, handoffs, retries, errors, and final-stop reason |
For each row, compare the expected event with the recorded event. The first failed comparison is your initial debugging target. Later symptoms may disappear when that boundary is corrected, so avoid “fixing” them independently until the sequence is coherent.
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Why does my AI agent keep looping?
A loop is a repeated request/result cycle with no progress toward a terminal outcome. Read the trace as a timeline and determine whether the model is repeating, the application is repeating, or the model is receiving information that makes repetition rational.
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When the same tool request repeats
Compare the tool name and arguments byte for byte across turns. Identical requests suggest that the model is not seeing a state change, not receiving the prior result, or receiving a result that does not answer the request. Check that each result is associated with the correct call and appears in the next model input.
When arguments change but the task does not progress
Inspect whether the tool returns an error, an empty payload, stale data, or an irrelevant record. A model may keep trying because the returned information does not establish success or failure. Log the semantic content supplied to the model, not only an HTTP status code.
When the application repeats the request
A retry policy, queue redelivery, timeout handler, or duplicate callback can execute the same operation even though the model asked only once. Use a request or call identifier and an idempotency strategy for side-effecting operations. Distinguish a new model turn from an application-level retry in your trace.
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When the runner never reaches a stop condition
Inspect the branch that handles successful tool results, tool errors, handoffs, and empty responses. A runner can continue because it treats every result as non-terminal, or stop too late because a terminal state is not propagated. Add explicit logging for the reason each iteration continues and the reason a run ends.
When a limit hides the real cause
An iteration or time limit can prevent an infinite run from consuming resources, but it does not explain why progress stopped. Record the limit event and the last complete request/result pair, then debug that pair. Do not raise a limit before confirming that each cycle changes the available information or state.
Why isn’t my AI agent calling tools?
Check tool availability, the model’s decision, the contract, and the execution path separately. “The tool was registered” is not proof that the model saw a suitable definition or that the application could run it.
Verify the tool was present in the actual request
Inspect the serialized model request for the tool name, description, input schema, and any tool-choice setting. Compare it with the request from the failing turn; middleware can remove or alter tools between turns, handoffs, or model providers.
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Check whether the task really supplied enough information
A model may reasonably answer from context when the requested operation is unnecessary, or decline a call when required arguments are missing. Make required fields explicit in the schema and description. Avoid descriptions that promise capabilities the implementation does not provide.
Validate arguments before execution
Capture the raw model arguments and the validation error. A malformed date, missing identifier, or unexpected enum value is a contract failure, not evidence that the model ignored the tool. Return a concise, machine-readable error to the model when recovery is possible; keep internal stack traces out of the model context.
Trace the callback and result message
If the request exists but no execution span follows, inspect routing, authentication, network access, and exception handling. If execution succeeds but the model still acts as if nothing happened, inspect the next conversation payload for the tool result, its call identifier, and its serialization.
Check handoffs and parallel paths
An agent may hand work to another agent or service before a tool call. Confirm which component owns the tool at that point and whether the handoff preserved the relevant conversation and definitions. A trace that reconstructs the end-to-end session is more useful than separate logs that cannot be joined.
How do I stop an AI agent from hallucinating?
Start with the exact unsupported claim and ask what evidence was available at the moment it was generated. A fluent answer is not evidence that retrieval, search, or a tool actually supplied the fact.
Define the claim precisely
Copy the sentence, identify the entity, date, quantity, quote, citation, or causal statement that is unsupported, and record the model turn that produced it. Separate a factual error from a reasonable interpretation, a stale fact, and a tool failure.
Rebuild the evidence chain
- Check whether retrieval, search, or a domain tool was invoked.
- Check whether it returned current, relevant content rather than an error, empty result, or stale cache.
- Check whether that content was included in the model’s actual context on the turn that made the claim.
- Compare the claim with the available evidence and mark any portion that goes beyond it.
OpenAI’s Help Center defines hallucinations as factually inaccurate outputs, including fabricated quotes, studies, citations, or references, and advises verifying important information. Its warning is direct: “Confidence isn’t reliability: The model may express high confidence even in incorrect answers.”
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Fix the broken evidence boundary
If evidence was unavailable, repair retrieval, permissions, freshness, or context assembly. If evidence was present but contradicted, make the contradiction visible and require the agent to state uncertainty or ask for clarification. If the model simply exceeded the evidence, tighten the response contract—for example, require claims to be supported by supplied passages and allow an explicit “not enough evidence” outcome.
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The same visible hallucination can arise from unavailable evidence, poor retrieval, stale context, a failed tool execution, or a response that overreaches. The trace narrows where the evidence chain broke; reproducing the particular system is still necessary to establish the application-specific cause.
What should a useful agent trace contain?
A useful trace lets you reconstruct the session without guessing. At minimum, retain the relationships among session, turn, model generation, tool call, tool result, handoff, and stop event.
- Model inputs and outputs: the messages and tool definitions sent to the model, plus the raw generation and parsed decision.
- Tool details: tool name, arguments, call identifier, start and end times, status, result, and error classification.
- Orchestration events: retries, handoffs, branch decisions, iteration counts, and terminal reason.
- Environment context: model and application versions, configuration identifiers, and relevant feature flags.
- Privacy controls: redaction or tokenization rules for personal, secret, and regulated data; retention and export permissions; and access logs for trace viewers.
OpenAI’s tracing documentation describes span-level inputs, outputs, statuses, durations, and tool request/result details when recorded. Product-specific retention, export, and data-handling behavior changes over time, so verify those settings in the documentation for the provider and version you deploy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I choose between logs, traces, and evaluations?
Use each layer for a different question rather than treating observability as a single product feature.
| Method | Best question | What to capture |
|---|---|---|
| Application logs | Did a callback, API request, or exception occur? | Correlation ID, operation, status, error, and timing |
| End-to-end traces | Where did the session first diverge? | Model inputs/outputs, tool arguments/results, handoffs, spans, and stop decisions |
| Datasets and evaluation runs | Did a change improve behavior without regressions? | Representative inputs, expected outcomes, trajectory checks, grading results, and version metadata |
When comparing observability implementations, evaluate visibility into model inputs and outputs; tool names, arguments, results, errors, and timing; end-to-end session and handoff reconstruction; trace export and retention; privacy constraints; and the ability to turn traces into repeatable evaluations. Availability differs by provider and framework version.
How should I fix the problem after finding the boundary?
Make the smallest change that addresses the first failed boundary, then rerun the same trace and your regression set.
- Model decision failure: clarify the tool’s purpose and required inputs, remove misleading tools, and test whether the model now selects the intended operation. Do not alter runtime code until the recorded request is appropriate.
- Contract failure: align the schema, validation, and implementation. Use one canonical field name and type across the model definition, parser, and function.
- Execution failure: repair routing, authorization, network access, or exception handling. Return a structured recoverable error; fail closed for unsafe side effects.
- Result-handling failure: preserve the call identifier, serialize the result predictably, and include it in the next model request. Verify this in the trace rather than assuming the SDK inserted it.
- Control-flow failure: define terminal outcomes, distinguish retryable from permanent errors, and log every continue/stop decision. Keep a resource limit as a safety guard while fixing the underlying transition.
- Evidence failure: repair retrieval or context assembly, or constrain the answer to supplied evidence. Add a test for unsupported claims and for correct refusal when evidence is absent.
After each change, compare the original failing case with the prior version. A broad prompt rewrite or a larger retry budget can mask the symptom while introducing a new tool-selection or factuality regression.
How do I turn a failure into a repeatable evaluation?
Convert a representative trace into a multi-turn test with an input, an expected task outcome, and checks on the trajectory—not only the final prose.
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Store the user input, relevant initial state, tool fixtures or controlled service responses, model and application versions, and the expected terminal outcome. For a hallucination case, include the evidence that should be available and the claims that must not be made.
Grade the trajectory
Check whether the appropriate tool was selected, arguments were valid, execution occurred once or according to the intended retry policy, results were returned to the model, handoffs were correct, and the run stopped. Also grade factual support for the final answer.
Run before and after the change
Compare the same cases with the old and new versions. Keep cases that already worked, because a local fix can silently break another path. If outputs vary, run enough repeated trials to observe the behavior rather than trusting one pass.
OpenAI recommends moving from individual traces to datasets and evaluation runs when repeatability and comparison matter. Anthropic’s January 9, 2026 discussion of agent evaluations emphasizes that multi-turn actions can modify state and allow errors to propagate, making trajectory checks more informative than single-turn scoring.
What is a practical debugging checklist?
- Can you replay the exact failing input and configuration?
- Do you have one trace that joins every model, tool, handoff, retry, and stop event?
- What is the first divergence between expected and actual behavior?
- For a missing call, was the tool in the actual request and was its schema usable?
- For a loop, do requests repeat, do results change, and does the next model turn see each result?
- For a hallucination, what evidence was present in context when the claim was generated?
- Does the fix address the boundary rather than only the final symptom?
- Does a multi-turn evaluation cover the failure, trajectory, terminal state, and previously passing cases?
Frequently Asked Questions
Should I change the system prompt before looking at logs?
Usually no. A trace can show that the prompt was adequate but a callback failed, a tool result was dropped, or a handoff changed the available context. Prompt changes are most useful after the recorded model decision itself is identified as the first incorrect transition.
What information should I redact from agent traces?
Remove secrets, access tokens, personal identifiers, and regulated data before storage or export. Keep stable placeholders and correlation IDs so turns remain joinable, and document who can view raw versus redacted payloads.
How can I test an agent when the model output is nondeterministic?
Use fixed fixtures for tools and retrieval, compare trajectory properties rather than exact wording, and run the same case repeatedly. Grade required actions, evidence support, and stop behavior with tolerances appropriate to the task.
Can raising the maximum iteration count stop a loop?
It can postpone termination but does not restore progress. First determine whether the model repeats, the application retries, or the returned information fails to change the next decision; then keep a limit as a safety boundary while correcting that cause.
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