Test an AI workflow at two levels: use deterministic tests to verify the orchestration your application controls, then run integration and end-to-end checks against real providers and external systems. Record traces that connect model calls to tools, handoffs, and application work, and check the final environment state—not just the model’s response.
Start by defining what success means
For each workflow, specify the input, acceptable behavior, and observable success condition. Draw a boundary between what your application owns—such as routing, retries, and tool execution—and what belongs to an external model, provider, or service. That distinction determines which test can provide meaningful evidence.
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For example, an application can assert that a tool call was dispatched with the expected arguments. It cannot establish, using a scripted model alone, that a live model will choose that tool for the same prompt.
Choose the right level of test
| Approach | What it validates | Repeatability and cost | What to assert | Main blind spot |
|---|---|---|---|---|
| Deterministic scripted test | Application-owned orchestration and normalized interactions | High repeatability for a fixed script; can run in memory without provider requests | Calls, arguments, handoffs, retries, guardrails, streamed events, and whether expected scripted steps were consumed | Does not prove a live model will make the same choice |
| Integration or end-to-end check | Real provider, protocol, or external-system behavior | Requires real adapters or an integration environment; results may vary with live models, services, and state | Real serialization and provider interaction, task outcome, and resulting external state | More variable and harder to diagnose without structured traces |
Use scripted tests for the orchestration you own
Use scripted models or test doubles to drive known sequences through your runner. Assert that it sends the expected inputs, invokes the right tools with precise arguments, follows or rejects handoffs as intended, applies guardrails, handles retries, emits expected streaming events, and manages session behavior. Check that the expected scripted steps were consumed; otherwise, a test may pass without exercising the path you intended.
#1 Best Overall
OpenAI’s Agents SDK testing documentation recommends scripted tests for application- and SDK-owned orchestration, including tool execution, handoffs, guardrails, retries, streaming, sessions, and other workflow capabilities. Treat these tests as evidence about your code and its controlled interactions—not as proof of live model behavior.
Use integration checks at real boundaries
Exercise a real provider adapter or integration environment where the distinction matters: wire serialization, external protocols, actual model choices, and interactions with systems outside your test double. Keep these checks separate from deterministic tests so that a mock-based pass cannot be mistaken for validation of a live dependency.
Rank #2
Capture traces that make a failure diagnosable
A useful trace links the workflow run to the events that shaped it: model calls, tool calls and outputs, handoffs, guardrail decisions, and custom spans for meaningful application operations. Include enough context to identify which workflow and variant ran. A transcript alone may show what the model said while concealing where routing or execution went wrong.
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Use representative failures to locate the break
Inspect runs that reveal a concrete failure: an unsuitable tool choice, incorrect arguments, a misplaced handoff, an instruction not followed, or a safety boundary that did not behave as expected. Trace grading can attach structured criteria to runs and help expose workflow-level regressions. A trace is most useful when it lets you identify the failing stage and turn that failure into a check.
Turn useful logs into repeatable evaluations
Curate realistic examples from development and observed failures into a dataset. For each case, define what counts as success and use an assertion or grader suited to that task. Then compare runs across prompt, model, or routing changes, and repeat trials when outputs can vary. OpenAI’s evaluation best-practices guide puts the sequence plainly: “Log as you develop so you can mine your logs for good eval cases.”
- Use task-specific cases that represent actual use rather than relying on a handful of convenient examples.
- Check instruction following, functional correctness, tool selection, argument precision, and handoff accuracy where relevant.
- Calibrate automated graders with human review. A generic score or a subjective impression that the workflow “seems to work” is weak evidence.
- Run evaluations as behavior changes, and inspect the underlying traces when a score or assertion regresses.
Evaluation thresholds should be designed for the task, not copied from an illustration. OpenAI’s guide gives an example using 1,000 held-out transcript-summary pairs, a ROUGE-L threshold of 0.40, and an 80% coherence threshold; those are example criteria, not reported findings or general recommendations.
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For workflows that change external state—such as creating a record or updating a system—assert the resulting state directly in the relevant environment. A successful-looking transcript is not proof that the action happened. Anthropic’s agent-evals article describes the outcome as “the final state in the environment at the end of the trial.” In practice, make the state check part of the end-to-end test alongside checks of the response and trace.
Best Value
Keep platform guidance current
Evaluation interfaces and SDK details can change. OpenAI’s evaluation best-practices page currently publishes a schedule under which the Evals platform becomes read-only for existing users on October 31, 2026, and is scheduled to shut down on November 30, 2026. These are published future dates, not guarantees; check the current page before choosing an implementation path. The agent-workflow guide separately describes a trace-first approach followed by datasets and repeatable eval runs, so verify which evaluation surface is current before following platform-specific steps.
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