Use both layers, but make each test answer a question at the boundary it can control. Unit-style scripted tests are the fast, repeatable choice for application-owned behavior: tool code and validation, orchestration, handoffs, guardrails, retries, sessions, and workflow shape. End-to-end evaluations with a real model are required when model judgment matters, including instruction following, tool selection, multi-turn decisions, and whether the task actually succeeds. Add provider integration tests for behavior owned by an external adapter or runtime, such as request serialization, authentication, sandbox lifecycle, network recovery, and audio transport.
A scripted workflow can prove that your pipeline handles a specified sequence of model outputs. It cannot prove that a production model will choose that sequence. The reliable approach is a layered suite: deterministic regressions for code you own, model evaluations for variable decisions, and real-provider checks for external boundaries.
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The boundary determines the test
Start by asking who owns the behavior under investigation. If your application or orchestration code owns it and the inputs can be made deterministic, a unit-style or scripted workflow test gives the clearest signal at the lowest cost. If the behavior depends on a model deciding what to do, run an evaluation with a real model and explicit success criteria. If the claim concerns a provider, transport, or runtime, exercise that real boundary with an integration test.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches| Decision axis | Unit-style scripted test | End-to-end evaluation or integration test |
|---|---|---|
| Main question | Did our deterministic code and orchestration do what we specified? | Does the real model or provider-backed system complete the task acceptably? |
| Model interaction | Fixed or scripted responses and test doubles | A real model for model-quality questions; a controlled real adapter for wire behavior |
| Best targets | Tool validation and execution, handoffs, guardrails, retries, workflow ordering, request construction at normalized boundaries | Tool-choice quality, instruction following, task success, multi-turn behavior, and final environment state |
| External systems | Replaced or scripted at a documented boundary | Real provider or transport, sometimes with controlled network conditions |
| Repeatability | High when scripts and inputs are fixed | Model behavior can vary; use datasets, graders, and multiple trials where appropriate |
| Main blind spot | Cannot establish real model quality or provider fidelity beyond the replaced boundary | Slower and more operationally involved; broad failures are harder to localize without traces |
| Good evidence | Assertions over public calls, tool effects, state, and script completion | Trace or transcript, grader results, environment outcome, and provider responses |
This is not a choice between two mutually exclusive test suites. The layers answer different questions and should be retained together when the risk justifies them.
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What unit-style scripted tests can prove
A scripted test supplies predictable model outputs while allowing the real SDK run loop and your application pipeline to execute. It is therefore stronger than a test that mocks the entire agent runner, but narrower than a production evaluation. The OpenAI Agents SDK Python testing documentation describes the intended use this way: “Use ScriptedModel when a test should exercise the SDK run loop, tools, handoffs, guardrails, sessions, retries, or normalized streaming without depending on a model provider.”
Tool implementation and argument validation
Script a tool call with representative arguments and assert the validation behavior, authorization checks, side effects, and result conversion. Include malformed arguments, missing fields, boundary values, and a tool failure. Because the model output is fixed, a failure points to your parser, tool, or orchestration rather than to a change in model behavior.
Orchestration and workflow ordering
Use a sequence of scripted turns to exercise the complete run loop: model output, tool invocation, tool result, the next model turn, and final response. Assert that required steps occur in order, that a result is passed to the next turn in the expected normalized form, and that the run terminates only after the intended work is complete.
Handoffs and guardrails
Represent the handoff decision in the script, then verify that the receiving agent gets the documented context and that the original agent stops or resumes as designed. For guardrails, provide inputs that should pass, fail, or trigger escalation. Assert both the decision and the externally visible effect, such as a blocked tool call or a safe fallback message.
Retries, sessions, and streaming normalization
Force a retryable error and check the retry condition, attempt count, backoff hook, and eventual result. Run a multi-turn scripted session to verify that state is retained, isolated between sessions, and cleared when the session ends. If your application consumes normalized streaming events, assert the assembled output and event ordering rather than provider-specific wire fragments.
Make the script itself an assertion
A passing test can still be misleading if the application exits early and leaves scripted turns unused. The Python SDK guidance recommends assert_complete() so the test fails when an expected turn was not consumed or the workflow terminated unexpectedly. Treat unconsumed script steps as evidence of a broken path, not as harmless test data.
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How to write a useful scripted workflow test
- Define the application question. State the deterministic behavior you want to prove, such as converting a tool result, routing a handoff, or applying a retry policy. Do not phrase the test as “the model will choose the right tool”; that is a model-quality claim.
- Choose the narrowest replacement boundary. Replace the model decision with scripted outputs, but keep your real run loop, tool registry, handoff code, guardrails, session store, and result handling in the test.
- Construct realistic turns. Include the tool call shape, arguments, tool result, and any follow-up turn needed to reach a final state. Keep the script readable enough that a failure explains the intended path.
- Assert observable behavior. Check public calls, validated arguments, side effects, state transitions, emitted events, final output constraints, and error handling. Avoid asserting private implementation details that can change without changing behavior.
- Require script completion. Call the SDK’s completion assertion or an equivalent check so an early exit and leftover expected turns fail the test.
- Keep external dependencies controlled. Use fakes or deterministic transports for databases, queues, email, and HTTP services unless the test is specifically about that external boundary.
The result is evidence that your code handles a known sequence correctly. It is not evidence that a real model will generate that sequence.
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When an end-to-end evaluation is necessary
Run a real-model evaluation whenever the behavior under test is a choice made by the model or a property of the completed task. The JavaScript SDK testing guidance makes the limitation explicit: a scripted function_call represents the model’s decision in the test; it does not establish that a real model will follow the instructions or select the tool.
Tool selection and instruction following
If the question is “Did the agent pick the right tool?” or “Did it decline an unsafe action?”, a scripted call assumes the answer and can never detect a wrong choice. Build representative tasks, run the real model, capture its trace, and grade the selected tool, arguments, policy behavior, and final result.
Task completion over several turns
Many agent failures emerge only after several decisions: an early mistaken assumption produces a bad search, which leads to an incorrect update, which is then reported as complete. Evaluate the complete task, not just the final text. For state-changing work, inspect the resulting environment state directly.
Prompt, routing, and policy changes
When you change a system prompt, routing rule, tool description, or model configuration, compare repeatable evaluation runs rather than relying on a handful of scripted regressions. The evaluation should include cases that distinguish the old and new behavior, plus adversarial or ambiguous requests where the change could create regressions.
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Designing an agent evaluation that produces evidence
Anthropic’s engineering guidance defines a useful vocabulary for multi-turn evaluation: a task is one test with inputs and success criteria; a trial is one attempt; a grader scores performance; a transcript is the complete record of a trial; and an outcome is the environment state at the end. Keeping these artifacts separate prevents a plausible response from being mistaken for a successful task.
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Write explicit tasks and success criteria
Describe the starting context, available tools, constraints, and the observable result that counts as success. A criterion such as “answer is helpful” is difficult to grade consistently. Prefer checks such as “the correct record is updated,” “no restricted tool is called,” “the handoff includes the required fields,” or “the response cites the retrieved values without inventing fields.” Include failure conditions and safety requirements.
Use graders suited to the behavior
Use deterministic graders for state, schema, permissions, and tool arguments. Use model-based or human review only for qualities that cannot be checked reliably with rules, and document the rubric. A grader should report why a trial passed or failed, not just a score, so failures can become targeted regressions.
Run multiple trials
Generative systems are variable: the same input can produce different outputs. Anthropic’s guidance therefore recommends multiple trials for important tasks. Record the number of attempts, pass rate, failure modes, and distribution of outcomes rather than treating one successful run as proof.
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A transcript shows what the agent said and which tools it called. The outcome shows what actually happened in the environment. For a file operation, check the file; for a database task, query the resulting row; for a message, verify the delivery record. A response that says “done” is not evidence that the state change occurred.
Provider integration tests cover a third boundary
Some failures are neither application-logic bugs nor model-quality problems. They belong to the provider adapter or runtime and require a real integration boundary.
Model adapter and HTTP behavior
Keep serialization, authentication headers, provider defaults, error mapping, and streaming or response parsing on the real adapter. A controlled transport can make these tests repeatable while still exercising the code that talks to the provider. A mock of your own adapter cannot prove that the wire payload is accepted or that provider errors are handled correctly.
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Sandbox lifecycle and isolation
If an agent runs in a sandbox, test startup, filesystem fidelity, process limits, teardown, and isolation against the actual provider environment. A scripted sandbox session can prove your orchestration reacts to a sequence of events; it cannot prove that the provider prevents escape or reliably creates the requested runtime.
Network recovery
Use an integration environment or controlled network conditions to exercise timeouts, dropped connections, partial responses, retries, and idempotency. Assert that recovery does not duplicate side effects and that a permanently unavailable service produces a clear terminal error.
Realtime and audio transport
Raw realtime or audio behavior depends on the transport and codec path. Test it on a real transport or integration environment, including connection setup, interruption, frame ordering, buffering, and shutdown. Normalized streaming tests remain valuable for application logic, but they do not establish fidelity of the underlying audio or realtime connection.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Concrete scenarios: choose the layer that matches the question
A tool-result conversion bug
Use a scripted workflow. Supply a known tool call, run the real tool pipeline, and assert the converted result delivered to the next model turn. This isolates conversion and orchestration. Add a real-model evaluation only if you also need to know whether the model will select that tool for representative requests.
The agent chooses the wrong tool
Use a real-model evaluation with tasks that require competing tools. Grade the selected tool, arguments, and final outcome from the trace. A scripted function call would force the supposedly correct choice and hide the defect.
A prompt or routing change
Use repeatable datasets and evaluation runs, then inspect traces for changed decisions. Keep a deterministic regression for any application bug discovered during analysis, but do not substitute that regression for the evaluation of model behavior.
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A sandbox escapes isolation
Use the actual sandbox integration boundary and verify the environment outcome. A scripted sandbox test can check that your code handles a reported violation; it cannot establish provider isolation.
A provider startup failure
Use a provider integration test with controlled failure conditions. Assert startup errors, retry behavior, cleanup, and the message surfaced to the caller. A fake provider response tests only your error branch, not the provider’s lifecycle contract.
A layered test plan for an AI-agent project
- Every change: deterministic unit tests. Cover pure functions, schemas, authorization, tool implementations, argument validation, state transitions, and error mapping. These should run quickly and explain failures locally.
- Every workflow change: scripted SDK tests. Exercise the real run loop, handoffs, guardrails, retries, sessions, and normalized streaming with fixed model turns. Require complete script consumption and assert side effects.
- Every prompt or model change: evaluation runs. Run a representative task dataset with explicit graders. Capture traces or transcripts, run multiple trials where variability matters, and compare outcomes with the previous version.
- On adapter or infrastructure changes: provider integration. Exercise authentication, serialization, streaming, sandbox behavior, network recovery, and audio or realtime transport at the real boundary.
- After a production failure: preserve the right evidence. Turn an application defect into a deterministic regression, a model-choice defect into a dataset and grader case, and an external-boundary defect into an integration test. Keep the original trace or provider response when it is safe to do so.
This layering follows the cheapest-test principle without weakening coverage: use the least expensive layer that can substantively answer the question, and retain higher-boundary checks wherever nondeterminism or external ownership is the risk.
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OpenAI’s workflow-evaluation guidance recommends starting with trace grading while debugging. A trace records model calls, tool calls, guardrails, and handoffs, allowing questions such as whether the right tool was chosen or whether a handoff happened at the intended point. Trace grading helps locate the failing decision before you invest in a broad evaluation run.
Once the team knows what good behavior looks like, move to datasets and repeated evaluation runs to compare prompts, routing logic, or other changes consistently. A dataset should contain the task inputs and expected criteria, while the trace explains the path and the outcome verifies the result. Do not reduce the comparison to final prose alone when the agent can change state or call privileged tools.
Common mistakes and how to correct them
- Calling a scripted test end to end. Correction: name it a scripted workflow or integration test and document the replaced model boundary.
- Asserting the model’s decision in a unit test. Correction: assert how your system handles a specified decision; evaluate whether a real model makes that decision separately.
- Mocking the adapter while claiming provider compatibility. Correction: add a controlled test through the real adapter and transport.
- Checking only the final response. Correction: inspect tool calls, handoffs, guardrail events, traces, and the final environment state.
- Running one model trial. Correction: use multiple trials for important tasks and report variability and failure modes.
- Ignoring leftover scripted turns. Correction: require
assert_complete()or an equivalent completion assertion. - Using broad end-to-end tests to diagnose every failure. Correction: reproduce the failure at the narrowest boundary that owns it, then retain the broader test when it protects against recurrence.
- Treating a successful claim as a successful operation. Correction: verify the resulting file, record, message, or other environment state directly.
Bottom line for test planning
Use unit-style scripted tests for deterministic behavior you own: tools, validation, orchestration, handoffs, guardrails, retries, sessions, and workflow shape. Use real-model end-to-end evaluations for decisions and outcomes that depend on model behavior, including tool choice, instruction following, multi-turn recovery, and task completion. Use provider integration tests for authentication, wire payloads, runtime lifecycle, isolation, network recovery, and realtime or audio transport. A robust AI-agent suite is the combination of these layers, with traces and environment outcomes supplying the evidence that a passing assertion alone cannot provide.
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