The Tool Desk
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What an execution trace shows—and what it cannot prove
OpenAI’s Evaluate agent workflows documentation describes a trace as “the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run.” In practice, a trace can help answer where a workflow went wrong: whether the agent chose an unsuitable tool, missed a handoff, or encountered a guardrail.
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A trace records what the system captured, not necessarily everything that happened. If an important event is missing from instrumentation, the trace cannot establish it. And a plausible-looking sequence of calls does not show that the task was completed correctly. Judge the run against its task-specific outcome criteria as well as its workflow decisions.
Evaluate the workflow and the final outcome
Use criteria tied to the task rather than treating a generic score as a universal measure of agent quality. OpenAI’s evaluation guide frames useful questions this way:
- “Did the agent pick the right tool?”
- “Did a handoff happen when it should have?”
- “Did the workflow violate an instruction or safety policy?”
- “Did a prompt or routing change improve the end-to-end behavior?”
The first questions concern decisions within the run; the last concerns the result across a workflow change. Pair them with a rubric for the actual task—for example, whether the requested information was found and whether the final response satisfies the user’s requirements. A tool call can be appropriate while the overall task still fails, so do not collapse process and outcome into one judgment.
A practical trace-based evaluation loop
1. Capture the events needed to reconstruct a run
Choose instrumentation that preserves a clear run boundary and the events relevant to your workflow. The OpenAI Agents SDK tracing guide documents spans for runner invocations, tasks, turns, agent activity, model generations, function calls, guardrails, handoffs and audio activity. These are SDK-specific details; other frameworks may expose different events and schemas.
2. Inspect representative failures while debugging
Open individual traces for runs that succeeded, failed, or behaved unexpectedly. Follow the sequence to locate the decision point: Did the agent select the wrong tool? Was a required handoff absent? Did a guardrail or instruction change the result? Looking at the run is useful for forming a diagnosis, but one trace is not enough to show that a change reliably improves behavior.
3. Write explicit graders for important decisions
Define what counts as correct for the task and, where useful, for individual spans or decisions. Grade relevant tool choices, handoffs, instruction adherence and safety behavior against those criteria. OpenAI’s trace grading documentation describes structured scores and labels for traces and spans. A grader—human or automated—applies the criteria you provide; it does not make correctness self-evident.
4. Build a repeatable evaluation set
Once the team can state what “good” means, preserve a representative set of examples and apply the same criteria across runs. This lets you compare prompt, routing or workflow changes against comparable cases instead of relying on a single memorable example. Include cases that exercise important branches, such as tool selection and handoffs, rather than only easy successes.
5. Change the workflow, then rerun the set
Use the traces and grading results to investigate a specific failure. A remedy might involve refining a prompt, changing available tools, adjusting routing or revising guardrails. Rerun the same evaluation set after the change and examine both task outcomes and workflow decisions; a score change is useful only in light of the criteria and examples behind it.
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Protect sensitive data in traces
Traces can contain prompts, model outputs, tool arguments and other data from a run. Decide what may be captured and who can access it before enabling tracing in production. In the OpenAI Agents SDK’s Python tracing guide, trace_include_sensitive_data is documented as true by default, with options to disable sensitive-data capture. The guide also states that tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy. Check the current documentation and your organization’s applicable settings before deployment.
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When assessing an observability or evaluation setup, focus on capabilities that affect your workflow rather than assuming products are interchangeable. Useful questions include:
- Does it capture the tool calls and handoffs you need to diagnose?
- Can you grade both individual spans and whole-run outcomes?
- Can you rerun comparable datasets as prompts or workflows change?
- Can you export or interoperate with the systems your team uses, including OpenTelemetry where needed?
- Are data controls, hosting and retention suitable for the information in your traces?
LangSmith’s product information describes observability and evaluation capabilities, including OpenTelemetry-related options and hosting choices. Those are vendor-stated product details, not a neutral head-to-head assessment. An archived OpenAI-Langfuse cookbook example illustrates an integration approach, but it may refer to outdated models or APIs; consult current vendor documentation before adapting its steps. The available material does not establish a neutral product benchmark, so compare tools against your own trace coverage, evaluation needs and data requirements.
Where trace evaluation is still evolving
Trace schemas and evaluation methods are not settled standards. The AAAI-26 AgentGraph paper proposes turning execution logs into interactive knowledge graphs linked to exact trace spans. It describes qualitative failure analysis and recommendations, as well as robustness evaluation using perturbations and causal attribution. This is a proposed research system, not independent evidence that graph visualization improves production agent quality.
The 2026 survey From Agent Traces to Trust reviews provenance representation, evidence attribution, tool-use provenance, runtime guardrails, memory provenance, observability and failure diagnosis. It identifies open problems such as unified trace schemas, claim-level provenance, realistic trace benchmarks, recovery-oriented evaluation and privacy-aware audit infrastructure. For practitioners, the implication is straightforward: make criteria and data handling explicit, and do not treat one trace format or score as a settled universal standard.
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