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The reliable way to debug a stuck or misbehaving AI agent is to treat every run as an inspectable trace. Capture the model request and response, selected tool and arguments, tool result or error, handoffs, guardrail decisions, timestamps, duration, status, turn count, and token usage. When a run fails, inspect the timeline to find the first divergence from the intended plan. That tells you whether to change routing, tighten a schema, repair a dependency, or bound retries—rather than guessing from the final error.
This guide shows how to instrument an agent, classify loops and timeouts, diagnose incorrect tool calls, and operate traces safely in production.
What should an AI-agent trace contain?
A useful trace follows one run from the user’s input to its final status. OpenAI’s Agents SDK tracing model includes the overall workflow, each model call, tool calls and outputs, handoffs, guardrails, and custom spans. For debugging, those events need enough context to answer six questions: what the model decided, which tool it selected, what arguments it sent, what came back, how long each step took, and why the next step or stop occurred.
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Minimum event fields
- Run identity: correlation ID, conversation or session ID, agent name and version, environment, and deployment region.
- Model decision: model identifier, prompt or policy version, response status, finish reason, and token usage when the runtime exposes it.
- Tool execution: tool name and version, validated arguments, start and end times, duration, result status, retry count, and error class.
- Control flow: turn number, handoff target, guardrail result, approval state, and the event that caused the run to stop.
- Safety context: redaction status and access classification for every captured input, output, and error.
Store raw prompts, arguments, and results only when your retention and access policies allow it. Otherwise, preserve hashes, field names, sizes, and structured error details so operators can still reconstruct the sequence without exporting secrets or personal data.
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Why does an agent get stuck in a loop?
Most apparent “hangs” are one of five different conditions. Classify the trace before changing code; each condition needs a different remedy.
| Trace pattern | Likely failure | What to verify | First corrective action |
|---|---|---|---|
| Repeated model and tool spans with unchanged arguments | Runaway planning or a retry loop | Turn number, previous result, state mutation, and retry policy | Set a maximum-turn budget and detect repeated state or identical tool arguments |
| Generation span ends with timeout, failed, or incomplete status | Model timeout or incomplete response | Prompt size, provider latency, network path, and downstream waits | Remove avoidable prompt growth and dependency delays; raise the timeout only after measuring them |
| Tool span is slow, errored, or returns an invalid payload | Tool timeout or tool failure | Per-tool duration, HTTP/database status, payload validation, and retry count | Give the tool its own deadline, typed output, concise error, and bounded idempotent retry |
| Model emits malformed JSON or an unexpected tool-related message | Malformed or unexpected tool call | Raw response, schema-validation result, and tool registry version | Validate before execution and return a structured correction request |
| Run stops at a policy boundary | Guardrail tripwire or approval requirement | Which guardrail fired, rule version, and approval state | Surface the policy reason to operators; do not treat an intentional stop as a transient error |
Detecting a real loop
A loop is more than a long run. Compare consecutive decisions and tool arguments. If the agent repeats the same action without a state change, record a repeated-call signal and stop or ask for clarification. A turn budget is still required because a loop can alternate between two different calls while making no progress.
Distinguishing latency from deadlock
Use start and end timestamps on every span. A single slow dependency produces one long tool span; a deadlock usually shows an open span with no completion event, or many short retries around the same dependency. Compare model and tool timelines so a model timeout is not misdiagnosed as a provider problem when the model was waiting on your application.
How do I enable tracing in an agent runtime?
Use the tracing facility built into your server-side agent framework when available, and use OpenTelemetry when you need one trace format across vendors and services.
OpenAI Agents SDK applications
Tracing is enabled by default in the normal server-side SDK path. Verify that traces are actually emitted in JavaScript browser runtimes and test environments, where defaults differ. Confirm the exporter, then create one correlation ID at request entry and carry it through model, tool, handoff, and guardrail spans.
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The runtime exposes distinct failure conditions such as maximum-turn exhaustion, ModelTimeoutError, malformed JSON or other unexpected tool-related failures, ToolTimeoutError, and input/output guardrail tripwires. Preserve that condition in the run status instead of collapsing every failure into a generic exception.
Organizations using OpenAI APIs under a Zero Data Retention policy should verify tracing availability for their setup; the Python tracing documentation states that tracing is unavailable under that policy. If traces cannot be retained, emit a local, redacted event stream that follows your policy and keeps the same correlation IDs.
OpenTelemetry for multi-vendor systems
OpenTelemetry provides a common span and attribute model when an agent calls more than one model provider or when tools run in separate services. Anthropic documents tracing Messages API requests, tool uses, and Managed Agents sessions with OpenTelemetry spans. Its SDK guide describes one span per request and tool use, with your application’s exporter sending those spans to the observability system you select.
Propagate the trace context across queues and HTTP calls. A tool service that starts a new unrelated trace makes the most important handoff invisible, so inject and extract the standard context headers at every boundary.
Recommended span attributes
run.id,conversation.id, agent and version, model, environment, and region.- Prompt, policy, and tool-schema version identifiers; store content only after redaction.
- Tool name and version, normalized arguments, result status, error class, retry count, and approval state.
- Start and end timestamps, latency, turn number, handoff target, input and output token counts, and estimated cost where available.
- Sampling and redaction indicators so an operator knows whether a field is complete, hashed, or omitted.
Do not put API keys, authorization headers, payment data, or unredacted personal information in span attributes. Restrict trace access by role and set retention periods separately for metadata and content.
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How can I find the first wrong decision in a trace?
Debug one reproducible run end to end. The final error is often only a symptom; the first incorrect tool choice, argument, or interpretation is the useful event.
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- Mark the intended plan. Write down the expected tool sequence and the state that should change after each tool result. This is your comparison baseline.
- Locate the first divergence. In timeline order, find the first model decision that selects a different tool, omits a required argument, ignores a result, or hands off incorrectly.
- Check validation before execution. Determine whether the arguments passed schema validation, whether unknown fields were rejected, and whether units, identifiers, and permissions were normalized.
- Inspect the tool result and timing. Compare the returned payload with the declared output schema. Check dependency status, duration, deadline, and retry count for hidden failures.
- Read the next model decision. A concise structured error should tell the model whether to correct the call, ask the user, or stop. If the next decision ignores that instruction, the prompt, tool description, or routing constraint needs repair.
- Apply one narrow fix and replay. Change the smallest relevant component—description/schema, router, tool implementation, timeout, retry, or turn budget—then replay the same case and verify the intended sequence in the new trace.
- Promote the case to a regression set. Keep the input, expected tool choice, allowed arguments, expected stop condition, and privacy-safe trace so later model or prompt changes are evaluated against it.
How do I stop an agent from retrying forever?
Bound retries at two levels: the entire run and each individual tool. A maximum-turn budget guarantees eventual termination; a per-tool attempt limit prevents one dependency from consuming the whole budget.
Use state-aware loop detection
Hash a canonical representation of the relevant state and normalized tool arguments. If the same state-and-action pair recurs without a permitted mutation, stop with a diagnostic such as “no progress after N attempts” and either ask the user for new information or escalate. For alternating calls, detect a repeated sequence rather than only identical adjacent calls.
Make retries safe
- Retry only transient failures such as explicit rate-limit or network conditions, and use exponential backoff with jitter.
- Require an idempotency key for writes that may be retried.
- Never blindly repeat a non-idempotent action such as sending a message, charging an account, or deleting data.
- Record the reason, delay, and attempt number in the trace.
- Return a terminal, operator-visible status when the budget is exhausted; do not silently continue in a background task.
Why does the agent choose the wrong tool?
Incorrect selection usually comes from ambiguous tool boundaries, weak schemas, or an unhelpful failure response. Make the tool catalog easier to route than to misuse.
Write tool contracts for routing
- Give each tool a distinct name and a one-sentence purpose that says when to use it and when not to.
- Document required arguments, types, units, allowed ranges, identifier formats, permissions, and side effects.
- Separate read-only tools from write tools. Put consequential writes behind an explicit approval state.
- Use strict schemas that reject unknown or unsafe arguments before execution.
- Declare output fields and error types so the model can distinguish “not found,” “invalid request,” “temporarily unavailable,” and “permission denied.”
Return actionable errors
Errors should be short, structured, and addressed to the next decision. Include a machine-readable class, the offending field, the allowed form, and a directive such as correct, ask_user, or stop. Avoid returning stack traces or vague text such as “tool failed,” which encourages arbitrary retries.
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Test near-neighbor choices
Build routing tests with ambiguous requests, missing information, overlapping capabilities, wrong units, and permission boundaries. Score both selection and outcome: a syntactically valid call to the wrong tool is still a failure. Keep the chosen tool, normalized arguments, and tool result in the trace so evaluators can grade the decision.
What should a production dashboard monitor?
Track rates and distributions by agent version, model, tool, and deployment. A single aggregate success rate can hide a failing tool or a new loop.
| Signal | Why it matters | Useful breakdowns |
|---|---|---|
| Run success and failure rate | Shows overall reliability and newly introduced failures | Agent version, model, region, error class |
| p50 and p95 latency | Separates normal performance from tail stalls | Whole run, model span, each tool |
| Model and tool timeout counts | Identifies deadline pressure and dependency degradation | Timeout type, service, endpoint |
| Maximum-turn exits | Reveals loops and plans that cannot converge | Turn budget, repeated-state signature |
| Tool error and invalid-argument rate | Exposes broken contracts and routing mistakes | Tool, schema field, error class |
| Repeated-tool-call rate | Finds identical or alternating retry loops | Tool pair or sequence, run version |
| Handoff, guardrail, and approval stops | Shows control-flow and policy friction | Rule, target agent, approval outcome |
| Token usage and cost | Highlights prompt growth and runaway planning | Model, run status, tool path |
Alert on sustained increases in failed or incomplete generations, tool timeouts, repeated identical calls, unusual token growth, or guardrail trips. Use a time window and minimum volume so one unusual request does not page an operator, and include a link or ID that opens the relevant trace.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do traces become repeatable evaluations?
Start with traces while diagnosing a live case. Once you understand what “good” looks like, convert representative traces into a dataset with expected decisions and outcomes. Run that dataset whenever you change the model, prompt, tool schema, router, or policy.
Trace grading assigns structured scores or labels to an end-to-end trace, including model decisions, tool calls, handoffs, and policy adherence. Grade tool selection, argument correctness, result handling, stop behavior, and latency separately so one successful final answer does not conceal an unsafe write or an unnecessary retry.
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Include difficult cases: ambiguous wording, malformed tool results, slow dependencies, permission denials, guardrail tripwires, and maximum-turn exhaustion. Store only the redacted fields needed for evaluation, and version the grader with the agent so score changes are explainable.
What privacy and retention controls are required?
Tracing can duplicate everything an agent sees and does, so treat it as production data. Redact secrets and personal data before export, encrypt transport and storage, restrict access to operators who need it, and define deletion and retention rules. Keep a separate audit record for approvals and consequential writes when policy requires it.
When content cannot be retained, preserve metadata such as event type, schema version, status, duration, token counts, and stable hashes. That still supports latency, loop, and error-rate monitoring without exposing the original conversation.
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Pre-release checklist for an observable agent
- A single correlation ID links the run, model calls, tools, handoffs, guardrails, and approvals.
- Every tool validates strict input and output schemas before and after execution.
- Tool descriptions state purpose, boundaries, units, permissions, side effects, and failure responses.
- Maximum turns, per-tool deadlines, and bounded retries are enforced in code.
- Writes are idempotent where possible and require approval when consequences warrant it.
- Trace fields are redacted, access-controlled, encrypted, and retained according to policy.
- Dashboards show latency, timeout, loop, invalid-argument, guardrail, token, and cost signals.
- Representative traces have graders or regression tests for routing, arguments, outcomes, and stop conditions.
- An operator can replay a privacy-safe failing case and identify the first divergence from the intended plan.
Frequently Asked Questions
Can I debug an agent without storing the full prompt and tool output?
Yes. Keep redacted or hashed content alongside structural metadata such as event type, schema version, status, latency, token counts, and error class. This supports loop and timeout diagnosis while limiting exposure of secrets and personal data.
Should a timeout be fixed by simply increasing the deadline?
Not automatically. First determine whether prompt growth, provider latency, a slow tool, or a blocked downstream dependency consumed the deadline; increasing it can make a loop or queueing problem worse.
How can I tell whether a bad answer came from the model or a tool?
Compare the first divergent model decision with the tool’s validated arguments and returned payload. If the selection or arguments were wrong before execution, routing or schema guidance is implicated; if the call was valid but the payload was wrong or late, inspect the tool and its dependencies.
What is the safest way to test a new tool in production?
Expose it first as a read-only or approval-gated capability, log every selection and argument-validation result, and set a small per-tool attempt budget. Promote it only after near-neighbor routing cases and failure responses pass regression evaluation.
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