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To debug an AI agent, start with one failing run and follow its end-to-end trace: model calls, tool inputs and results, handoffs, guardrails, and relevant application spans. Find the first point where the run diverged from the expected path, inspect the code at that boundary, and grade representative traces against explicit criteria. Turn recurring failures and expected behavior into a dataset you can rerun after workflow changes. Before tracing real users, decide what data may be captured and configure sensitive-data controls.
What you need to diagnose a misbehaving agent
A final answer alone rarely tells you which part of an agent workflow failed. A useful trace records the run as a sequence of decisions and events, including model calls, tool calls, handoffs, guardrail events, and custom spans around important application code. OpenAI’s Agents SDK documents this end-to-end trace model, with tracing enabled by default in its normal server-side path. See the Agents SDK tracing documentation and its integrations and observability guide.
Tracing helps locate where a particular run went wrong; it does not, by itself, prove why the behavior occurred. You need to connect trace events to the code that built prompts, selected tools, transformed results, routed work, or accepted the final response. Then use graders and repeatable evaluations to test whether the behavior meets the task’s requirements.
1. Make one failure reproducible
Choose an actual run that clearly demonstrates the problem. Record enough context to compare it with later runs and understand what changed.
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- The user’s request and the expected outcome.
- The observed outcome and the specific way it was wrong or incomplete.
- The trace identifier, if your tracing system provides one.
- Relevant versions and configuration: agent code, prompt, model, tools, and routing or guardrail settings.
Keep the original example intact. Avoid broad prompt rewrites before identifying the step that failed: a change might mask the symptom without correcting the underlying boundary.
2. Read the trace in sequence
Follow the run from start to finish, looking for the first event that departs from the expected path. Review the model’s input and output, each tool’s arguments and result, any handoff between agents, guardrail events, and custom spans around application logic. The sequence matters: a poor final answer may originate in an incorrect interpretation, an inappropriate tool choice, a bad tool result, or a later transformation.
- Model interpretation: Did the model misunderstand the request or miss a required constraint?
- Tool selection and arguments: Was the right tool chosen, and were its inputs valid and complete?
- Tool result: Did the tool return the expected data, or did an error or transformation alter it?
- Handoff and routing: Was work sent to the right agent or workflow stage, and did control return as expected?
- Guardrail or application boundary: Did a check, formatter, validator, or application decision block or change the result?
OpenAI describes traces as a way to inspect the decisions and tool activity across an agent run. Its documentation explains the trace structure, but the trace remains evidence to investigate—not proof of causation.
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3. Inspect and instrument the code at the failing boundary
Once you locate the first divergence, follow that event into the relevant application code. Check how the prompt was assembled, how tools were selected or validated, how tool output was transformed, how routing was applied, and what conditions allowed the final answer through.
If the trace omits context needed to distinguish these possibilities, add a custom span or ordinary structured logging at that boundary. Include useful, safe-to-record identifiers and decision metadata; avoid logging secrets or sensitive user content by default. OpenAI’s observability documentation covers integrations and custom instrumentation. Instrumentation can make a workflow easier to inspect, but you still need a reproducible example and targeted checks to establish which change fixes it.
4. Grade traces against explicit behavior
Turn the failure into criteria tied to the task, rather than relying only on whether the final answer sounds plausible. For example, assess whether the agent selected the correct tool, supplied valid arguments, handed off appropriately, respected instructions and safety constraints, and returned an answer supported by the tool result.
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OpenAI’s trace grading guide describes assigning structured scores or labels to a trace to assess correctness, quality, or adherence to expectations. Its agent workflow evaluation guide describes using selected traces and evaluation results to refine prompts, tool surfaces, routing, or guardrails. Grading traces can help distinguish a workflow failure from an answer that merely looks wrong at the end.
Use criteria that another reviewer could apply consistently. A rubric might define what counts as a correct tool choice or appropriate handoff, while a code-based check can verify a strict condition such as whether a required field is present. For ambiguous or judgment-heavy outcomes, human review may be useful alongside automated graders.
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5. Build a dataset and rerun evaluations
Individual trace inspection is a practical starting point. When failures recur—or when you change a prompt, model, tool, or routing rule—collect representative examples so you can compare versions consistently.
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- Include known failures, successful runs, and edge cases.
- For each example, store an expected outcome or a rubric specific to the task.
- Keep examples that exercise distinct workflow boundaries, not just variations of one easy case.
- Run the same evaluation after a change and compare the results against the previous version.
OpenAI positions datasets and evaluation runs as a repeatable way to benchmark changes and compare prompts over time. A dataset makes regressions easier to detect than relying on memory or manually checking a few recent conversations. See Evaluate agent workflows for its documented workflow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Decide what trace data may be stored
Trace payloads can include sensitive information. OpenAI’s Agents SDK documentation says generation spans can store LLM inputs and outputs, function spans can store function inputs and outputs, and audio spans include encoded input and output by default. The documented trace_include_sensitive_data setting can disable certain text capture; audio has a separate setting. Consult the current tracing documentation for the active SDK version and the exact settings available to your deployment.
Before enabling tracing on production traffic, decide which prompts, outputs, tool arguments and results, and audio may be recorded. Check export configuration, the receiving backend, access controls, retention, and redaction requirements as well as SDK settings; turning off a capture option does not establish how data is handled elsewhere in your pipeline.
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When to consider an observability platform
You can apply this workflow with your existing logging and tracing setup. If you evaluate hosted platforms, compare the details that affect your system rather than relying on a general claim of agent observability.
- Framework and instrumentation: Which languages and frameworks are supported, and can you use existing OpenTelemetry instrumentation?
- Trace coverage: Can you inspect model calls, tool inputs and results, routing, handoffs, guardrails, and custom application spans?
- Evaluation methods: Does it support curated offline datasets, online evaluation, code or heuristic checks, model-based grading, human review, and trajectory scoring relevant to your workflow?
- Data controls and deployment: What is captured, and what redaction, retention, access-control, regional, or self-hosted choices are available?
- Operational fit: Can results feed back into development, and does the platform provide the monitoring and visibility your team needs?
LangChain describes LangSmith observability as supporting multiple frameworks and OpenTelemetry, with dashboards for token usage, latency, errors, cost, and feedback. Its LangSmith evaluation page describes curated datasets, online evaluation, multiple grader styles, and human review, as well as managed, BYOC, and self-hosted arrangements across its product pages. These are vendor descriptions of its own capabilities; verify current functionality, availability, and data terms against your requirements.
An OpenAI cookbook example demonstrates a Langfuse tracing integration, but the cookbook is archived, so it should be treated as an example to investigate rather than a guarantee of current compatibility.
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