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You can test an AI agent without making another LLM request by replacing its model boundary with a scripted response or in-memory fake, then running the real orchestration code against that controlled input. The test can verify tool selection and arguments, handoffs, guardrails, retries, sessions, streaming events, and error handling while using no provider, sandbox, or Realtime API requests. Keep provider wire compatibility and genuinely model-dependent quality in a separate integration or live-evaluation layer.
This split makes unit tests fast, deterministic, inexpensive, and precise: a changed workflow either consumes the expected scripted steps or fails at the point where it diverges. Recorded traces and redacted datasets then let you replay important historical behavior without rerunning the agent.
What does it mean to test an AI agent without another LLM call?
An agent test without an additional LLM call runs your application loop with a deterministic substitute for the model. The substitute returns predefined assistant messages, emits selected tool calls, raises chosen failures, or responds to the request it receives. Your production runner, tool dispatcher, handoff logic, state store, and policy checks remain real.
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This is different from asking a cheaper or smaller model to grade the first model. A grader that invokes an LLM is still a model call and can vary from run to run. A scripted test instead checks observable behavior against an explicit contract.
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What the offline test can prove
- The runner sends the expected messages and tool definitions to the model interface.
- The agent chooses the intended tool, with valid arguments and the correct ordering.
- A tool result is incorporated into the next turn and the final state.
- A handoff transfers control to the correct specialist and returns when expected.
- Guardrails reject or modify unsafe input and the application follows its policy branch.
- Retries occur only for retryable failures and stop at the configured limit.
- Session memory, multi-turn state, streamed events, and cancellation are handled correctly.
- Unexpected model requests or an early exit are reported rather than silently accepted.
What it cannot prove
A fake does not establish that a provider serializes requests exactly as expected, that authentication works, or that a real model will select the same tool for ambiguous language. It also cannot measure emergent helpfulness, factuality, nuanced safety, latency, token usage, or distributional changes in model output. Those questions belong in integration runs, trace-based evaluation, or controlled live trials.
How should I design the replaceable model boundary?
Inject a model interface into the agent runner instead of constructing a provider client deep inside business logic. Production wiring supplies the real provider; unit tests supply a scripted implementation with the same normalized input and output types.
Define a narrow interface
The interface should accept the conversation, available tools, session metadata, and any response-format settings, then return a normalized assistant message, a function call, a stream event sequence, or a typed failure. Keep provider-specific request objects outside the orchestration layer so the test double does not need to imitate every wire detail.
class ModelPort:
async def respond(self, request) -> ModelResult:
...
class AgentRunner:
def __init__(self, model, tools, handoffs, session_store):
self.model = model
self.tools = tools
self.handoffs = handoffs
self.session_store = session_store
Normalize provider responses at this boundary. A function call should carry a tool name and parsed arguments; a text response should carry content and finish information; a stream should expose typed events. Tests can then assert application behavior without depending on a provider SDK’s internal classes.
Make each scripted step explicit
Give every scenario an ordered list of expected model steps. A step can be a fixed assistant message, a function call, a failure, or a request-aware responder that inspects the incoming request. The test should fail if the runner asks for an extra turn or exits before consuming all configured steps.
OpenAI’s Agents SDK documents this pattern with ScriptedModel, fixed response steps, request-aware responders, and call-history accessors such as calls, first_call, and last_call. Its deterministic, provider-neutral utilities run in memory and make no model, sandbox-provider, or Realtime API requests.
How do scripted model responses test tool calls and handoffs?
Return the exact function-call event that should drive the workflow, execute a fake tool, and then return the scripted follow-up message. Assertions should cover both the call itself and the state produced by the tool.
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- Start the runner with a user request and an in-memory tool registry.
- Configure the first model step as a function call, including the expected tool name and JSON arguments.
- Have the fake tool validate its arguments, record the invocation, and return a deterministic fixture.
- Configure the next model step as the final assistant response that uses that fixture.
- Assert the final response, the tool’s recorded arguments, and all state changes.
- Assert that every scripted step was consumed.
Argument assertions catch regressions that a final-text assertion can miss. For example, an agent might still produce a plausible answer after passing a date in the wrong timezone or omitting a required account identifier.
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Handoff scenario
Script the first agent to emit a handoff event to a named specialist. Run the real handoff dispatcher, give the specialist its own scripted response, and assert which agent owns the final message, which context was transferred, and whether control returned to the original agent. Include a case where the target is unavailable so the fallback path is exercised.
Detecting workflow drift
OpenAI’s testing utilities expose errors named UnexpectedModelCall when the application makes an extra request and UnconsumedModelSteps when it exits early. Treat either error as a test failure. This turns a hidden prompt or orchestration change into a specific, reviewable diff in the expected trajectory.
How can I test guardrails, retries, and failures deterministically?
Inject failures at the model, tool, policy, and transport seams, then assert the exact branch the application takes. Determinism comes from deciding in the fixture when the failure occurs and what the next response will be.
Guardrail and policy tests
- Provide input that should be rejected and script no model response; assert the rejection type, audit event, and absence of tool calls.
- Provide input that requires redaction; assert the sanitized payload sent to the model and the user-visible explanation.
- Return a borderline tool result; assert that post-response policy checks block publication or require escalation.
- Verify that a handoff cannot bypass the same guardrails applied to the primary agent.
Retry tests
- Configure the first model or tool step to raise a typed, retryable exception.
- Configure the next step to succeed with a known response.
- Assert the retry count, backoff decision, idempotency key, and absence of duplicate side effects.
- Repeat with a non-retryable exception and assert immediate failure.
- Exceed the retry limit and assert the terminal error, telemetry, and user fallback.
Use an in-memory clock or injected scheduler when retry timing is part of the contract. That lets you test deadline handling without sleeping in the test suite.
Malformed and adversarial outputs
Script invalid JSON arguments, an unknown tool name, an empty message, an unsupported content type, and a response that exceeds your application limit. The expected result should be a typed validation error or safe fallback, not an unhandled exception or an accidental external call.
How do I test sessions, multi-turn state, and streaming?
Keep the session store and event consumer real while supplying deterministic model turns or event sequences. This verifies state transitions and UI-facing behavior without relying on model variability.
Session and memory cases
- Run two turns under one session identifier and assert that only the intended history is visible on turn two.
- Run parallel sessions and verify that messages, tool results, and locks never cross session boundaries.
- Restart the runner between turns and assert that persisted state reconstructs the same context.
- Exercise a truncated or expired session and verify the documented recovery behavior.
- Assert that sensitive fields are excluded from persisted transcripts and logs.
Streaming cases
Have the fake emit a known sequence such as message-start, token chunks, tool-call-start, tool-call-delta, tool-call-end, and message-complete. Assert ordering, buffering, flush behavior, cancellation, and the final assembled message. Add a case where the stream ends mid-tool call or emits an error after partial text; the consumer should close resources and expose a consistent failure state.
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If your application coordinates a sandbox or realtime session, script the boundary that creates and controls it. Test command sequencing, timeouts, cleanup, and handoff of session identifiers. These tests prove your orchestration; a separate integration suite must still verify the provider’s actual protocol and sandbox fidelity.
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How do fake tools and external services make tests reliable?
Replace databases, HTTP services, filesystems, queues, and payment or notification endpoints with deterministic, inspectable doubles. Do not let a unit test reach a real network merely because the model is mocked.
Use in-memory implementations
An in-memory repository can enforce the same validation rules as production while storing records in a local map. A fake HTTP client can match method, path, headers, and body, then return a fixture or a selected error. A queue fake should record published messages and support deterministic delivery, duplication, and redelivery.
Assert inputs and resulting state
Check the complete argument object sent to each tool, including defaults and authorization context. Then inspect the resulting state: created records, updated session fields, emitted events, and audit entries. A final assistant sentence alone is not evidence that the side effect occurred correctly.
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- Inject a fixed clock for timestamps, expiry, and retry calculations.
- Inject a deterministic identifier generator when IDs appear in assertions.
- Use fixed locale, timezone, and random seeds where formatting or sampling matters.
- Provide explicit fixtures for missing, stale, duplicate, and partially populated records.
This separation follows the boundary described in OpenAI’s testing guidance: application-owned orchestration is suitable for offline tests, while provider serialization, network recovery, sandbox fidelity, and authentication require integration coverage.
What is trajectory matching, and when should I use it?
Trajectory matching compares the complete expected sequence of events with the sequence produced by the run. It is useful when a workflow has a known correct path and a different tool order or missing intermediate step is a defect.
Include user input, assistant outputs, tool calls, tool results, handoffs, guardrail decisions, and final state in the comparison. Normalize incidental fields such as timestamps, request IDs, and token chunk boundaries before comparing; keep semantically important fields exact.
LangChain describes its trajectory-match evaluator as a step-by-step comparison against a hard-coded reference trajectory. It requires no additional LLM call, so it is deterministic, fast, and cost-effective. Use it for workflows such as “look up an order, verify ownership, then issue a refund,” where the sequence is part of the requirement.
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| What you compare | Recommended match | Reason |
|---|---|---|
| Tool name and order | Exact | A reordered or skipped operation can change safety and state. |
| Validated tool arguments | Exact after canonicalization | Defaults, types, and identifiers are behavior contracts. |
| Tool result fixture | Exact or fixture ID | Keeps the scenario reproducible. |
| Natural-language final text | Structured assertions or approved variants | Wording can change without changing the owned behavior. |
| Streaming chunk boundaries | Tolerant, with ordered content | Transport chunking is usually incidental. |
Can I replay old agent traces without rerunning the agent?
Yes. Redact representative production traces, convert them into datasets or replay cases, and evaluate the stored conversations and outcomes without invoking the model again. This is valuable for regression testing after changing tools, prompts, policies, or state handling.
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What to capture
OpenAI describes traces as end-to-end records containing model calls, tool calls, guardrails, and handoffs. Preserve the event order, normalized inputs and outputs, tool results, policy decisions, latency metadata when useful, and the final state. Store a version of the redaction and normalization rules with each case.
How to redact safely
- Remove names, contact details, credentials, access tokens, payment data, and unique identifiers unless a stable surrogate is required for the test.
- Replace identifiers consistently within one trace so joins and ownership checks still work.
- Strip hidden system data that the test does not need, especially secrets embedded in tool results.
- Record the source date, application version, and scenario label so a failure can be reproduced without exposing the original user.
Historical evaluation platforms
Microsoft Foundry supports evaluating preexisting conversations, datasets, and historical traces without rerunning the model or agent. That capability is distinct from a live run: it measures the stored behavior against new graders or checks, while a live run measures what the current model would do now.
When do I still need a live model evaluation?
Use a real model when the question concerns behavior your application does not fully control: prompt changes, ambiguous intent, tool choice under unfamiliar wording, nuanced helpfulness, factuality, or safety judgments that depend on language context.
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Separate the evaluation layers
| Layer | Additional LLM calls | Best for | Main limitation |
|---|---|---|---|
| Scripted unit test | Zero | Orchestration, state, tools, guardrails, retries, error paths | Does not predict new model behavior. |
| Trajectory matching | Zero | Known, step-by-step workflows | Requires a defined reference path. |
| Trace or dataset replay | Zero for the agent run | Regression checks on historical behavior | Cannot reveal responses to inputs not in the dataset. |
| Provider integration test | Usually one or more | Serialization, authentication, network, sandbox, and protocol compatibility | Slower, costlier, and less deterministic. |
| Live model evaluation | Required | Prompt quality, emergent behavior, ambiguous language, helpfulness and safety | Variable outputs require repeated trials and careful grading. |
| LLM-judge evaluation | Required for the judge | Flexible assessment of nuanced responses | Judge variability and extra cost; less deterministic. |
Anthropic’s evaluation structure names four useful pieces: a task with defined inputs and success criteria, repeated trials, graders, and a complete transcript or trajectory. A transcript includes outputs, tool calls, reasoning or intermediate results, and other interactions. Repeated trials help account for variable model outputs, but they do not replace deterministic unit coverage.
How should I organize an agent test suite?
Organize tests by the behavior your team owns, not by the provider product. Keep fixtures small, named, and reviewable so a failure shows the intended contract.
Recommended test groups
- Routing: direct answers, tool selection, handoffs, and fallback routing.
- Validation: malformed arguments, missing permissions, unsupported tools, and schema changes.
- Policy: blocked inputs, redaction, post-tool checks, escalation, and audit events.
- Reliability: timeouts, retryable and terminal failures, cancellation, duplicate delivery, and cleanup.
- State: session isolation, persistence, expiration, concurrent turns, and recovery.
- Presentation: streaming event order, final assembly, structured output, and user-visible errors.
- Regression: redacted traces and datasets from important production scenarios.
What every scenario should record
- A human-readable purpose and the input category.
- The scripted model steps and why each step is expected.
- Fake-tool fixtures, injected failures, and clock or identifier settings.
- Assertions for calls, arguments, state, events, policy decisions, and final output.
- A requirement that all scripted steps are consumed and no unexpected calls occur.
CI execution strategy
Run scripted and trajectory tests on every change. Run redacted trace replay on pull requests that alter prompts, tools, schemas, guardrails, or session code. Schedule provider integration and live evaluations separately with explicit budgets, repeat counts, and failure thresholds. Publish call history and the smallest failing trajectory as CI artifacts so engineers can diagnose the divergence without reproducing a network request.
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A deterministic failure is useful only when its category is clear. Classify the first divergence rather than masking it by loosening every assertion.
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The test reports an unexpected model call
The runner took an extra turn, often because a tool result was not recognized, a termination condition changed, or a retry was triggered unexpectedly. Inspect the last consumed step and call history, then fix the loop or update the scenario only if the new turn is intentional.
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Scripted steps remain unconsumed
The workflow exited early because a guardrail blocked it, a handoff returned unexpectedly, or a parser accepted an incomplete response. Assert the intermediate status and add a specific scenario for the early-exit branch instead of deleting the unused step.
The final text passes but the behavior is wrong
Add assertions on tool arguments, state mutations, policy events, and trajectory order. Natural-language output can look correct even when the wrong record was read or a side effect was skipped.
A test is flaky despite a fake model
Look for uncontrolled time, random IDs, shared mutable fixtures, parallel session leakage, asynchronous task ordering, or a real network call in a tool implementation. Inject those dependencies and make event ordering explicit before changing the expected output.
A live evaluation disagrees with unit tests
That is not necessarily a contradiction. Unit tests prove that the application handles a particular model response correctly; live evaluation asks what a variable model will produce. Add the observed case to a redacted replay set, then decide whether the remedy belongs in prompts, tool descriptions, guardrails, or orchestration.
What is the practical workflow from a new agent to release?
Start with the smallest deterministic contract, then add realism only where the question requires it.
- Map the boundary: identify every model, tool, handoff, session, sandbox, and realtime interface.
- Normalize I/O: define typed results and failures that the runner can consume independently of a provider SDK.
- Write a happy-path script: include the expected model turns, tool calls, results, and final state.
- Assert the trajectory: require exact tool order and arguments, and require all steps to be consumed.
- Add failure branches: cover validation, guardrails, retries, timeouts, malformed outputs, cancellation, and handoff loss.
- Fake every side effect: use in-memory databases, HTTP fixtures, queues, files, clocks, and identifiers.
- Add session and stream cases: verify isolation, persistence, event ordering, and cleanup.
- Build a redacted replay set: include representative historical traces and edge cases.
- Run integration checks: verify provider serialization, authentication, network recovery, and sandbox or realtime protocols.
- Run live evaluations: use tasks, graders, repeated trials, and explicit acceptance thresholds for model-dependent quality.
- Gate releases: block on deterministic contract failures and review live-evaluation regressions with their traces.
How do I decide whether a test belongs offline or live?
Ask whether the expected result is an application invariant or a model-quality judgment. If a competent model response is already supplied and you need to verify what your code does with it, use a scripted test. If producing or judging the response is the thing under investigation, use a live evaluation.
- Offline: “Did we call the account lookup tool with the verified user ID?”
- Offline: “Does a timeout retry once and avoid duplicate billing?”
- Offline: “Does a blocked tool result trigger escalation and an audit event?”
- Live: “Will a prompt revision improve tool choice for ambiguous requests?”
- Live: “Is the answer helpful, safe, and factually complete across varied language?”
- Integration: “Does the provider accept our serialized request and return the stream correctly?”
The strongest test strategy uses all three layers without confusing their evidence. Deterministic tests protect orchestration from regressions; replay protects known real-world cases; live trials measure behavior that cannot be reduced to a fixed script.
Frequently Asked Questions
Will mocking the model hide prompt regressions?
It can, because a scripted model never tests whether a changed prompt elicits the intended response. Keep prompt-sensitive cases in a separate live evaluation set, and promote important discoveries from those runs into redacted replay cases for the surrounding orchestration.
How large should a recorded trace dataset be?
There is no universal size. Start with coverage of each workflow branch, permission level, failure mode, and a sample of common production inputs; then add every incident that reveals a new regression class. Track scenario categories and coverage rather than chasing an arbitrary number of traces.
Can deterministic tests measure token usage or provider latency?
No. An in-memory fake has neither real token accounting nor network timing. Measure those properties in provider integration runs, using the same prompts and representative payload sizes, and keep the measurements separate from correctness assertions.
How do I review changes to scripted trajectories?
Treat fixture edits like code changes: require a reason in the pull request, show the old and new event sequence, and identify which requirement changed. A trajectory should be updated only when the product contract changes, not merely to make a failing test green.
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