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To tell whether an AI agent improved, evaluate the old and revised workflows against the same representative dataset and scoring criteria, then inspect traces and examples to explain the differences. An MCP connection can give an agent access to tools and context; it does not determine whether the agent used them correctly or returned a correct answer. This guide uses OpenAI’s documentation as an implementation example, not a requirement to use its platform.
Start with the failed run, then test beyond it
Inspect a trace of the bad answer to reconstruct that specific execution: model calls, tool calls, handoffs, guardrails, and relevant custom events. For an MCP tool call, check which server and tool were selected and what arguments were recorded. Ask: “Did the agent pick the right tool?” Did it handle the returned data correctly, follow its instructions, and complete the task?
A trace is evidence about one run, not an estimate of general performance. Add the failure to a repeatable evaluation dataset alongside representative cases covering the behaviors the workflow is expected to handle. Preserve the inputs and the expected outcomes or scoring rubrics needed to assess them. A single anecdote is not enough to establish that the agent works better in general.
OpenAI’s agent evaluation guide describes using trace grading to investigate questions such as whether the right tool was chosen, a handoff happened when it should have, an instruction or safety policy was violated, or a prompt or routing change improved end-to-end behavior. Use the trace to diagnose a case; use a dataset and graders to compare behavior across cases.
#1 Best Overall
Define success with graders that fit the task
Turn the failure into explicit criteria before changing the workflow. A grader should measure the property you care about, rather than reward an answer merely for sounding plausible. OpenAI’s grader documentation describes several approaches:
- Exact or string checks: use for deterministic requirements such as a required phrase, field, or structure.
- Similarity measures: use when closeness to a reference answer is meaningful for the task.
- Model graders: use rubric-based scoring for qualities that require judgment.
- Python checks: use when a criterion can be expressed as programmatic logic.
- Combined graders: use when a task has both mechanical requirements and judgment-based dimensions.
Keep component scores visible. A composite score may make comparisons convenient, but it can hide a regression in an important dimension—for example, a higher overall score alongside worse tool selection or instruction following. Document what each criterion means and how it is scored so the comparison remains interpretable.
Make one documented change at a time where practical
Choose a specific workflow element to revise: prompt instructions, the available tool surface, routing, or guardrails. Record what changed and why it is expected to address the observed failure. Changing one interpretable element at a time makes a score shift easier to investigate; when several elements must change together, document them rather than implying the evaluation isolates their individual effects.
MCP standardizes how applications provide tools and context to LLM applications, but it does not define whether a tool call was appropriate or an answer correct. The OpenAI Agents SDK guide describes MCP as “an open protocol that standardizes how applications provide context to LLMs,” and offers a USB-C analogy. Treat MCP as an interface, not a correctness check or a guarantee that a server is safe.
Choose the runtime and MCP connection for your deployment
OpenAI documents the Agents API, Agents SDK, and Responses API as different ways to build agent workflows. These are platform-specific choices, not universal categories every developer must use. Compare them by where execution occurs, integration effort, state management, and how tools are executed in the deployment you need. The OpenAI agents guide provides its platform’s runtime comparison.
MCP connection design also affects where execution and operational responsibilities sit. A hosted remote server and a server connected from the agent runtime can differ in server reachability, network boundaries, and where approvals are managed. OpenAI’s Agents SDK MCP guide and integrations and observability guide describe its supported approaches. Select based on the actual network and approval requirements; an MCP connection alone does not make an external server trustworthy.
Run the same evaluation before and after
- Save the baseline. Run the original workflow on the dataset and criteria you defined. Keep the run’s scores and enough example-level results to identify where it succeeds or fails.
- Apply and record the change. Note the prompt, tool, routing, or guardrail revision so readers can connect the comparison to the workflow version that was tested.
- Run the revised workflow on the same dataset. Hold inputs, graders, and scoring criteria steady. If any of them change, report that difference; otherwise the before-and-after scores are not directly comparable.
- Compare each criterion and inspect cases. Report sample size, raw counts, score values, changes by criterion, and examples that improved or regressed. OpenAI’s Evals API reference documents evaluation configuration and runs.
There is no universal percentage formula for describing an improvement. If reporting a percentage-point difference, show both underlying scores and label the difference accurately. A percentage-point change is not the same as a relative percent change.
Use traces and examples to explain the result
A useful report connects the measured result to observable behavior. Include the dataset size, baseline and revised scores, component-level changes, and representative examples or traces. Identify the workflow change that was tested, then distinguish what the evaluation measured from your interpretation of why it changed.
For example, an evaluation may show a higher score on a tool-selection criterion after a routing revision. The score and example traces are evidence of the observed difference; they do not, by themselves, prove that the routing revision caused it. Other workflow changes or variation between runs may matter. Avoid causal certainty unless the evaluation design supports it.
OpenAI’s Agents SDK tracing documentation describes traces that capture workflow execution, while its tracing documentation explains inspecting traces in the dashboard, including MCP tool-call details. Check the current documentation and configuration for your implementation because product surfaces can change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check trace availability and sensitive data handling
In the normal path, OpenAI documents Agents SDK tracing as enabled by default, with global, code-level, and per-run controls for disabling it. The documentation also says tracing is unavailable to organizations using OpenAI APIs under a Zero Data Retention policy. Confirm that your organization’s policy and SDK configuration permit the traces your debugging workflow depends on.
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A practical report structure
Keep the report concise enough to scan but detailed enough to audit. Include:
Quick Recap
- Evaluation setup: dataset size and coverage, grader criteria, and whether the same inputs and scoring rules were used in both runs.
- Workflow change: the prompt, tool surface, routing, or guardrail change that was evaluated.
- Results: baseline and revised scores, raw counts, and changes for each criterion.
- Evidence: representative traces or examples, including meaningful regressions as well as improvements.
- Interpretation: what the evidence suggests and what it cannot establish about why the result changed.
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