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Debug a bad AI-agent result by treating the entire run as the unit of analysis. Follow its execution tree from the root run through model calls, tool invocations, handoffs, retrieval, memory activity, and the final response; find the first point where what happened diverged from what the task required. A trace can show that sequence, but it does not automatically expose every memory system or prove why a model chose an action.
What should an agent trace tell you?
A useful trace makes the sequence and nesting of meaningful work in one run visible. The OpenAI Agents SDK documentation describes built-in traces that collect LLM generations, tool calls, handoffs, guardrails, and custom events. OpenAI’s session-tracing documentation also describes agent spans with model and tool activity nested beneath them, so a tool call can be read in the context of the agent that made it.
That hierarchy is more useful than a flat list of log lines: it lets you ask what the agent was doing, which step came next, and what result was available to later steps. It is evidence about the execution, not a complete explanation of the model’s internal reasoning. Trace fields and coverage depend on the SDK, application instrumentation, and configuration.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsHow do you isolate the failed run?
Start with one concrete run, not a broad claim that “the agent is broken.” Preserve enough identifying information to reproduce and compare it. This is an implementation recommendation, not a universal schema required by the cited SDK documentation.
- Assign or retain a stable run or session ID.
- Record the application version, prompt or configuration version, model identifier when available, timestamp, and outcome label.
- Capture the input and relevant dependency versions in a manner that complies with your privacy and retention rules.
- When practical, replay the same input with the same code, prompt, model, tools, and index versions before changing anything.
Label the observed outcome precisely: for example, wrong answer, wrong tool selected, tool error, missing evidence, stale memory, timeout, or incomplete run. A specific label narrows the investigation without assuming the cause.
How do you read the execution tree?
- Open the root run. Confirm the input, outcome, and whether the run completed, errored, or stalled.
- Follow child activity in order. Inspect model calls, tool calls, handoffs, guardrails, and subagent work under the agent that performed them. OpenAI’s session observability and tracing documentation describes inspecting turns, tools, subagents, and traces.
- Mark the first divergence. Compare the intended task and expected state with the observed output at each step. The earliest mismatch is usually a more productive lead than the final bad answer.
- Follow consequences forward. Check whether later steps received and used the result of the divergent step, or whether a later step introduced a separate failure.
For example, a final answer that invents a detail could follow several different paths: retrieval returned no relevant passage, a tool returned an error that was overlooked, or relevant evidence was retrieved but not used faithfully. The final response alone cannot distinguish these cases.
How do you diagnose a tool-call failure?
Inspect each invocation within its agent or subagent context. Separate the decision to call a tool from the tool’s execution and from the model’s use of the returned result.
- Selection: Was this the appropriate tool for the task, or did the agent choose the wrong action?
- Arguments: Were the inputs valid, complete, and consistent with the tool’s expected contract?
- Execution: Did validation pass? Did the call succeed, fail, time out, or retry? Check the response and status available in your instrumentation.
- Interpretation: Was the returned result correct, and did the next model step interpret and use it correctly?
- Handoff: If work moved to another agent, did the receiving agent get the necessary context and result?
These checks help distinguish a bad tool choice from a runtime failure and from a correct tool response that was misunderstood. The official OpenAI trace documentation supports inspecting tool calls in the agent tree, but the exact fields visible depend on the SDK and trace configuration.
How do you diagnose a RAG failure?
Trace retrieval and generation as connected but distinct stages. LangChain describes LangSmith visibility into RAG pipelines; that vendor description does not establish a universal debugging standard or guarantee that every deployment exposes every field below. Treat the following as a diagnostic procedure and verify what your own instrumentation records.
- Check the corpus and index. Confirm the intended corpus or index and its version were queried, rather than assuming the right data was available.
- Inspect the retrieval request. Review query construction and any filters that may have excluded relevant material.
- Review the retrieved passages. Examine the chunks, ranking, and source metadata. Decide whether the retrieved evidence actually addresses the question.
- Compare evidence with the response. If relevant passages were retrieved, check whether the generated answer used them faithfully, cited them where expected, or contradicted them.
- Compare against a known-good run. Apply the same evaluation criteria to a successful example to see whether the difference begins in retrieval or generation.
If useful evidence never arrived, investigate ingestion, chunking, query construction, filtering, or retrieval and ranking. If the evidence was available but the answer misrepresented it, focus on the generation step and how the retrieved context was presented. Do not infer which stage failed from the answer alone.
How do you find memory failures?
Do not assume an ordinary model-call trace automatically records an application’s arbitrary memory store. The reviewed OpenAI documentation describes custom trace events and trace-data controls, but does not establish built-in lineage for every memory system. To debug memory, instrument reads and writes explicitly.
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- Connect the memory event to the run that consumed or changed the item.
- Check whether the relevant item was missing, stale, conflicting with another item, or scoped to the wrong user, task, or session.
- Use redaction or references instead of storing sensitive memory payloads in traces when the payload is not needed for diagnosis.
These records let you distinguish a retrieval problem in the memory layer from a model problem involving memory that was successfully supplied. An item that was never read cannot explain the model’s response as consumed context; an item that was read may still have been ignored or misinterpreted.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should teams compare observability options?
Evaluate tools against the debugging work your team needs to do. The capabilities below are vendor-described in the cited documentation, not independent performance findings; confirm field-level support, permissions, and configuration for your own deployment.
| Evaluation area | Question to verify | Documented evidence |
|---|---|---|
| Trace coverage | Can you inspect model calls, tool calls, handoffs, guardrails, and custom events? | OpenAI Agents SDK documentation lists these event types in its built-in tracing description. |
| Hierarchy and context | Can you see which agent or subagent performed a model or tool step? | OpenAI tracing documentation describes agent spans and nested activity. |
| RAG visibility | Can the trace show retrieval alongside generation, with enough detail to diagnose your pipeline? | LangChain describes LangSmith visibility into RAG pipelines; verify the details available in your deployment. |
| Interoperability | Can traces be exported or connected to existing observability infrastructure? | OpenAI documents OTLP JSON export for session traces, subject to enablement and permission requirements. LangChain describes OpenTelemetry support. |
| Metrics and evaluation | Can you compare cost, latency, errors, and feedback across runs? | LangChain’s overview describes LangSmith dashboard metrics including token usage, latency percentiles, error rates, cost breakdowns, and feedback scores. |
| Privacy and access | What inputs and outputs are captured, how can they be omitted or redacted, and who can export traces? | OpenAI documents sensitive-data controls and export permission requirements. |
How do you protect sensitive trace data?
Decide what to record before enabling broad payload capture. The OpenAI Agents SDK documentation says sensitive-data capture is enabled by default and describes disabling it so request input and response output are omitted from model spans. Review the applicable configuration and access controls for your deployment, and avoid placing secrets or unnecessary personal data in custom events. Reducing payload detail can make some investigations harder, so retain only what your privacy and debugging requirements justify.
How do you confirm a fix?
Turn each incident into a repeatable regression case rather than closing it after one successful replay. Include the original input, the expected tool or retrieval behavior, and a measurable success criterion. Compare traces after changes to code, prompts, models, or indexes, so you can identify which change affected the run.
Where available, monitor failure rates, latency, cost, and user feedback across runs. LangChain’s product overview lists these as LangSmith dashboard metrics; those are described product capabilities, not independent evidence of performance. A regression check should still verify the task outcome, not just a healthy-looking trace or lower error count.
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