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Use both layers. Unit-style tests are the fast, deterministic way to verify code and orchestration that your team owns: tool validation and execution, handoffs, guardrails, retries, sessions, and workflow ordering. End-to-end (E2E) evaluations use a real model and, when necessary, real providers to answer different questions: will the agent follow instructions, choose an appropriate tool, complete a multi-turn task, and leave the environment in the required state?
A scripted model can exercise your actual agent loop without making a network model call, but a scripted tool call is an assumption about what the model decided. It cannot prove that a production model would make that decision. Keep provider integration checks for behavior that belongs to the provider or transport, such as serialization, authentication, sandbox isolation, streaming, and network recovery.
The boundary determines the test
AI-agent systems combine ordinary software with probabilistic decisions. A validator, retry counter, session store, or handoff function can usually be made deterministic. Tool selection, instruction following, and the quality of a final plan depend on model behavior and can vary between trials. Testing the wrong boundary creates false confidence: a green scripted workflow may only show that your code handled the exact calls you supplied.
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| Question | Best first test | Evidence to keep |
|---|---|---|
| Did our tool reject malformed arguments and return the expected result? | Unit-style test with fixed inputs | Validation errors, side effects, returned value |
| Did the SDK run loop perform the expected handoff, guardrail, retry, or session transition? | Scripted-model workflow test | Public calls, state changes, consumed script, final result |
| Will a real model select the right tool and follow the policy? | Real-model evaluation with representative tasks | Trace, transcript, grader scores, task outcome |
| Did the provider serialize, authenticate, stream, or start a sandbox correctly? | Provider integration test | Actual request/response, transport events, environment state |
| Did a complete customer task finish correctly over several turns? | End-to-end evaluation, often with multiple trials | Final environment state plus transcript and trace |
This division follows current guidance from the OpenAI Agents SDK documentation for Python and JavaScript, OpenAI’s workflow-evaluation guidance, and Anthropic’s multi-turn evaluation terminology.
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What unit-style tests can establish
Tool validation and execution
Test the function that receives model-supplied arguments as ordinary application code. Cover required fields, types, ranges, authorization checks, idempotency, error conversion, and side effects. Then test the adapter that converts the tool’s result into the message shape expected by the next model turn. A useful regression can script one tool call, run the real tool pipeline, and assert the exact converted result passed onward.
This proves your implementation behaves correctly for that call. It does not prove that a model will discover the tool, understand its description, or supply suitable arguments.
Orchestration and handoffs
When one agent delegates to another, deterministic tests can verify routing rules, context transfer, return handling, and termination. Supply controlled inputs and assert which public handoff operation ran, which instructions were attached, and what state was retained. Include negative cases: an invalid destination, a missing context field, a loop that exceeds the allowed depth, and a handoff rejected by policy.
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Guardrail tests should isolate each policy decision: what is accepted, blocked, redacted, or escalated. Retry tests should cover transient versus permanent errors, maximum attempts, backoff decisions, cancellation, and preservation of the original failure. A scripted model lets you exercise the complete run loop around those decisions without paying for or depending on a provider response.
Sessions and workflow shape
Verify that conversation state is loaded and saved at the correct points, that a new session cannot see another user’s data, and that a resumed run does not duplicate a side effect. Assert ordering where ordering matters: validation before execution, guardrail before external action, and persistence after a successful commit. These tests are especially valuable because a model can appear to recover from a workflow defect while leaving inconsistent state.
How to use a scripted model correctly
The Python Agents SDK testing documentation states: “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.” The important phrase is “exercise the run loop.” You are testing the application and SDK boundary while supplying predictable model outputs.
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- Define the behavior under your control. Decide whether the case is about argument validation, a handoff, a retry, a session transition, or another deterministic rule.
- Write the smallest script that reaches that behavior. Represent each expected model turn, including tool calls and the final response needed to end the run.
- Run the production pipeline. Do not replace the tool registry, handoff mechanism, guardrails, or session implementation unless that replacement is the subject of the test.
- Assert observable effects. Check state, public calls, tool results, and the final output. Avoid asserting private implementation details that can change without changing behavior.
- Assert script completion. The Python guidance recommends
assert_complete()so a test fails when the run ends early or leaves expected turns unused. An unexpectedly successful early exit can otherwise hide a broken branch.
In the JavaScript SDK guidance, a scripted function_call is explicitly the model decision supplied by the test. Treat it as an input assumption, not as evidence of model quality. A passing test means your system handled that decision; it does not mean a real model would follow the instructions or select that function.
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What an end-to-end evaluation must measure
Model-dependent decisions
Use a real model when the question is “Did the agent pick the right tool?” or “Did it hand off when it should have?” Include tasks that distinguish plausible alternatives. For example, give the agent a request that can be answered from existing context, one that requires a read-only tool, and one that requires confirmation before a state-changing tool. Grade the decision, not merely the prose of the final answer.
Instruction following and policy adherence
Prompt changes, tool descriptions, routing instructions, and safety policies should be compared with a repeatable task set. A grader can check whether the agent asked for required confirmation, refused a prohibited action, preserved user constraints, and used the allowed data sources. A scripted call would bake the desired choice into the fixture and therefore miss a regression in instruction following.
Task completion and environment outcome
For a state-changing task, the final message is only one artifact. Inspect the resulting environment: the record created, file updated, permission changed, or workflow completed. An agent that says “done” without making the change has failed even if its text sounds convincing.
Anthropic’s engineering guidance, published January 9, 2026, uses precise terms that help structure these evaluations:
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- Task: one test definition with inputs and success criteria.
- Trial: one attempt at that task.
- Grader: logic that scores the trial.
- Transcript: the complete record of the attempt.
- Outcome: the environment state at the end.
- Evaluation harness: the machinery that runs tasks, records trials, applies graders, and captures outcomes.
Run multiple trials for important tasks because model outputs vary. A single successful conversation is an example, not a reliability estimate.
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Provider integration tests belong at the real boundary
A mock can verify that your code called an adapter. It cannot establish that the provider accepted the wire payload or enforced the behavior you rely on. Add integration coverage when the claim concerns an external system.
- Model serialization and HTTP: exercise the real adapter with a controlled transport and inspect the request shape, headers, authentication handling, errors, and response normalization.
- Sandbox lifecycle: use the actual provider integration to verify startup, filesystem fidelity, cleanup, and isolation. A scripted sandbox session cannot prove that one run is unable to access another run’s files.
- Streaming and realtime audio: test raw event ordering, reconnect behavior, buffering, interruption, and audio transport on a real transport or integration environment. A normalized stream fixture only proves your normalization code.
- Network recovery: inject controlled timeouts or disconnects at the transport boundary and verify retry, cancellation, and duplicate-side-effect protection.
Keep these checks narrower than a full production scenario. The goal is to prove provider behavior once at the boundary, while application behavior remains covered by fast deterministic tests.
A practical decision sequence
- Identify the owner. If your application owns the behavior and the inputs can be fixed, start with a unit-style test. Examples include parsing, validation, side effects, routing under controlled conditions, guardrails, retry conditions, and session transitions.
- Choose a scripted workflow test for orchestration. Supply fixed model turns, execute the real SDK and application pipeline, and assert state, public calls, and complete script consumption.
- Use a real-model evaluation when judgment matters. Build representative tasks with explicit success criteria and graders for tool choice, handoff quality, policy adherence, and completion.
- Add provider coverage for provider-owned claims. Use the actual adapter, provider, sandbox, or transport whenever fidelity, isolation, authentication, or wire behavior is part of the claim.
- Preserve the narrowest regression after a failure. An application bug becomes a deterministic test. A model-behavior failure becomes a task in the evaluation dataset. An external-boundary failure remains an integration test. Keep more than one layer when the incident crosses boundaries.
Concrete examples: choosing the right layer
Tool result conversion bug
Script a model tool call, execute the production tool pipeline, and assert that the result is converted into the exact structure sent to the next turn. This is a workflow test. It does not test whether a real model would choose the tool, so pair it with a model evaluation if tool selection is also a requirement.
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Create representative requests and run them through the real model. Grade the selected tool, its arguments, confirmation behavior, and the resulting state. A fixture that directly emits the “correct” function call would make this test pass even if the model’s descriptions or routing prompt had regressed.
Prompt or routing change
Run the same dataset before and after the change, using the same graders and recording traces. Compare decision quality, policy adherence, task outcomes, and failure categories rather than relying on a single final-response score.
Sandbox isolation failure
Run an integration test against the actual sandbox provider. Create files or processes in one session, attempt access from another, verify cleanup, and record startup and teardown failures. A scripted session cannot establish isolation guarantees.
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Use traces to debug, evaluations to compare
OpenAI describes traces as records of model calls, tool calls, guardrails, and handoffs. During debugging, inspect a trace to answer questions such as: Did the agent choose the wrong tool? Did a guardrail fire too late? Did a handoff occur twice? Was a retry caused by the adapter or by the model’s response?
Once the desired behavior is clear, move from one-off trace inspection to datasets and repeated evaluation runs. Keep the task inputs, grader definitions, model and prompt versions, traces or transcripts, and environment outcomes together. This makes a prompt or routing change comparable over time and prevents a favorable anecdote from becoming a release decision.
Designing multi-turn evaluations that reveal compounding errors
In a multi-turn agent, an early mistake can alter every later decision. A missing field, incorrect assumption, or unauthorized side effect may be carried forward as if it were true. Therefore, grade intermediate checkpoints as well as the final result when the task warrants it.
- Record the initial inputs and the required invariants.
- Define which tools may be called at each stage and what confirmation is required.
- Capture the complete transcript, including tool arguments and results.
- Check the environment after each irreversible action when possible.
- Score both process properties (appropriate tool, safe handoff, policy compliance) and outcome properties (correct final state).
- Repeat the task enough times to expose variation; do not treat one successful trial as proof of reliability.
This distinction also prevents a common reporting error: a transcript is not the same thing as an outcome. The agent’s claim that an operation succeeded must be checked against the system that was supposed to change.
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| Layer | Typical frequency | Purpose | Failure response |
|---|---|---|---|
| Unit tests | Every change | Fast checks of validators, tools, state transitions, guardrails, and retry logic | Fix application code before merging |
| Scripted workflow tests | Every change to prompts-as-data, orchestration, or SDK integration | Exercise the real run loop with predictable turns | Inspect consumed turns, public calls, and state |
| Provider integration | Scheduled, pre-release, or on changes to adapters and transport | Verify wire behavior, authentication, sandbox, streaming, and recovery | Separate provider or environment failure from application failure |
| Real-model evaluations | Per release, prompt/model change, or risk-triggered run | Measure tool choice, instruction following, policy, and task completion | Inspect traces, grader disagreements, and outcome state |
Use the cheapest layer that can substantively answer the question. Do not remove a slower layer when the risk it covers is external or nondeterministic.
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Calling a scripted test “end to end”
If the model response is fixed, describe the test as a scripted workflow or integration test. It may traverse your full application pipeline, but it does not include model decision quality.
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Mocking the boundary under investigation
If you are claiming that authentication, serialization, stream framing, or sandbox isolation works, replace only unrelated dependencies. Keep the adapter, transport, or provider behavior real at the boundary being verified.
Asserting only the final text
Check tool calls, guardrail decisions, handoffs, retries, and environment state. A polished sentence can conceal a missing side effect or an unsafe action.
Using one happy-path task
Add ambiguous requests, missing information, refusal cases, retries, conflicting instructions, and recovery from tool errors. Include tasks that require different tools so the evaluator can detect selection regressions.
Keeping no record of the failing run
Persist the task version, model and prompt identifiers, transcript, trace, grader outputs, and final state. Without those artifacts, a variable failure is difficult to reproduce or classify.
What to document for every test
- The boundary being tested and which component owns the behavior.
- Whether model outputs are scripted, simulated, or produced by a real model.
- Which external systems are mocked, controlled, or real.
- The success criteria and the exact assertions or grader rules.
- Expected side effects and the method used to verify them.
- Failure cleanup, retry limits, and protection against duplicate actions.
- For evaluations, the task set, number of trials, grader version, transcript or trace retention, and environment outcome.
Clear documentation stops a deterministic regression from being misread as evidence of production model quality and makes failures easier to route to the team that can fix them.
Bottom line
Unit-style tests and end-to-end evaluations answer different questions. Test deterministic, application-owned behavior with fast unit and scripted workflow tests. Use real-model evaluations for judgment, instruction following, tool selection, and complete task behavior. Test provider-owned behavior at the real adapter, sandbox, or transport boundary. Keep traces, transcripts, grader results, and environment outcomes so each failure becomes the right kind of regression. The reliable approach is a layered stack, not a choice of one test type over the other.
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