When an AI workflow gives an unreliable result, inspect the full execution trace and find the first step that departed from expectations. A final answer may reflect an earlier mistake in a model decision, tool call, tool result, handoff, or guardrail—not a failure in the last step. Fix that cause, then test the change across representative examples rather than trusting one successful rerun.
Why the final answer is not enough
A multi-step AI workflow can fail before its final response is produced. A model may choose the wrong tool, pass unsuitable arguments, receive an unexpected tool result, hand control to the wrong component, or encounter a guardrail outcome that changes what happens next. Later steps may simply be reacting to that earlier event.
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OpenAI’s Agents SDK tracing documentation describes a trace as an end-to-end record of a workflow run, made up of spans. Depending on the workflow, those records can include model generations, tool calls, handoffs, guardrails, and custom events. The goal is to inspect the actual sequence, not infer what happened from the final answer or the model’s explanation of it.
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- Choose a representative failure. Save the input and enough context to distinguish the workflow version and configuration that produced it. Avoid comparing runs if prompts, routing, tools, or other relevant settings changed without being recorded.
- Open the execution trace. Read its spans in order. Inspect model inputs and outputs, selected tools and arguments, tool results, handoffs, guardrail events, and any custom spans your workflow records.
- Find the earliest divergence. Identify the first point where the actual input, decision, call, result, or control transfer differs from what the workflow needed. Follow downstream events to understand the impact, but do not begin by changing the last step if its behavior was a consequence of an earlier event.
- State a testable hypothesis. For example: “The workflow selected the lookup tool instead of the calculation tool because the routing instruction is ambiguous.” A specific hypothesis gives you something to confirm or reject; changing several prompts, tools, and routing rules together makes the result harder to interpret.
- Make one targeted change and rerun the case. Compare the new trace with the original. Check whether the suspected divergence changed and whether the run now meets the intended acceptance rule.
Turn “unreliable” into observable checks
Before judging a fix, describe what a correct run should do in terms you can inspect. OpenAI’s guide to evaluating agent workflows suggests questions such as whether the workflow chose the right tool, handed off when appropriate, followed its instructions and safety policy, and improved after a prompt or routing change.
Choose a criterion that matches the failure, rather than grading only whether the final answer sounds plausible. Examples include:
- Tool selection: Did the workflow choose the required tool for this input?
- Handoff: Did it pass control to the appropriate agent or component when the task required it?
- Instruction compliance: Did the relevant output follow the instructions and safety policy?
- Task acceptance: Did the final result satisfy a concrete rule for this task?
These are examples, not universal metrics. A useful criterion is specific enough that two evaluators can judge it consistently and closely tied to the failure you are trying to correct.
Use traces to understand a run, and evaluations to test a change
A trace helps explain an individual execution. To check whether a workflow change works beyond that one example, preserve the failure and other representative cases in a dataset and run evaluations repeatedly. OpenAI recommends moving from individual traces to datasets and evaluation runs once you know what “good” looks like.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsTrace grading adds structured scores or labels to runs, making it easier to spot workflow-level issues and understand why a run passed or failed instead of treating the final response as a black box. Use the same criteria to compare runs before and after a targeted change, and check for regressions in cases that previously worked. A successful rerun of one failure is evidence about that case—not proof of broad improvement.
Keep trace data useful without overlooking privacy
Traces can contain sensitive information, including generation content and function-call inputs or outputs. Before collecting production traces, review what the SDK records, who can access it, and how long it is retained. OpenAI’s trace configuration documentation describes the Agents SDK’s sensitive-data setting. Also check its documented Zero Data Retention limitation before relying on that option for a data-handling requirement.
The right level of trace detail is the level needed to diagnose behavior while meeting your organization’s data controls. If sensitive fields should not be captured, determine whether they can be excluded or redacted in your stack before enabling production tracing.
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The specific trace fields, privacy controls, and evaluation features depend on the SDK and runtime. The cited OpenAI documentation describes the OpenAI Agents SDK and Platform; it does not establish that other frameworks expose the same controls or use the same setup. For another stack, look for equivalent support for end-to-end traces, tool inputs and outputs, handoffs, guardrails, structured grading, repeatable datasets, and data exclusion or redaction.
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When comparing tracing or evaluation options, check whether they capture the whole workflow or only final responses, whether they record the events relevant to your failure, whether they support repeatable grading, and whether their data controls and runtime support fit your requirements.
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