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Use both. Traditional unit and integration tests should protect deterministic contracts such as schemas, parsers, tool adapters, state transitions, generated-code compilation and known regressions. Agent evaluations should exercise the model-plus-harness on representative tasks, checking tool choices, handoffs, policy adherence, multi-turn behavior and the user-visible outcome. Neither replaces the other.
The practical rule is to put every criterion through the most reliable measurement available: exact assertions for objective conditions, trace or trajectory grading for process behavior, end-state checks for real side effects, and explicit rubrics (calibrated against human judgments) for qualities such as clarity or tone.
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The difference in one sentence
A traditional software test asks whether a known component behaves according to a stable contract. An agent eval asks whether a probabilistic system can complete a representative task, using the permitted tools and producing an acceptable result. The two methods overlap, but they observe different layers of the system.
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What traditional tests measure well
Deterministic component contracts
Use unit tests for parsing, validation, serialization, calculations and other functions whose inputs and expected outputs are known. Schema tests can assert required fields, data types and allowed values. A failing assertion points to a specific code change and can run quickly on every commit.
Integration boundaries
Integration and contract tests verify that an agent’s tools, databases and services agree on request and response formats. They can catch an authentication change, a renamed field, a timeout policy or an incompatible API before a model ever calls the service.
State invariants and generated artifacts
When an agent changes an environment, ordinary tests can verify invariants such as idempotency, authorization and transaction consistency. For coding agents, compile the generated code, run the project’s test suite and apply static checks. These objective checks are stronger than asking a language model whether its own code looks correct.
Known regressions
Keep a focused test for every defect that should never return. Deterministic tests are inexpensive, repeatable and easy to diagnose, so they belong close to the implementation and in continuous integration.
What agent evaluations add
Multi-step behavior
An eval can present a complete task rather than a single function call: interpret the request, gather context, choose tools, recover from an error and communicate the result. It can grade the sequence of actions as well as the final response.
Tool choice, handoffs and policy adherence
Capture the full trajectory and inspect whether the agent called the right tool with allowed parameters, respected a required approval step and handed work to another agent or person when policy required it. These are workflow properties that a unit test of an individual tool cannot establish.
Multiple acceptable answers
Many natural-language tasks have no single string that is “correct.” A rubric can assess grounding, completeness, clarity, safety and instruction following. Pairwise comparison or a judge model can rank two answers, but the criteria must be explicit and the automated grader should be calibrated against human ratings.
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Generative systems can produce different outputs for the same input. OpenAI describes this as a reason traditional testing alone is insufficient for AI architectures in its evaluation best-practices guide. An eval dataset and repeated trials expose variability that a single deterministic assertion would miss.
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Choose the method by the question you need answered
| Need | Prefer | Example check |
|---|---|---|
| A deterministic component contract | Traditional unit or integration test | Input validation, JSON schema, tool adapter or database invariant |
| A generated artifact with objective acceptance criteria | Traditional tests inside an agent eval | Does generated code compile and pass the project’s tests? |
| Tool selection, handoffs and policy adherence | Trace or trajectory grading | Was the right tool called with allowed parameters, and did a required handoff occur? |
| A task with a verifiable state change | End-state evaluation | Did the refund, booking or file change actually happen in the environment? |
| Multiple acceptable natural-language answers | Rubric, pairwise comparison or calibrated judge | Is the answer grounded, clear and compliant with policy? |
| Quality under real-world use | Offline regression plus online monitoring | Do curated cases remain healthy, and are production anomalies promoted into the suite? |
This is not a choice between two competing test suites. The same task can contain all three layers: deterministic checks for the tool and state, trace grading for the route taken, and a rubric for the response shown to the user.
Build a combined testing loop
1. Define success in observable terms
Write down the task, the input and the conditions that constitute success before selecting a grader. Separate required end-state conditions from process preferences and subjective quality. For a support workflow, “the account contains one authorized refund” is an end-state requirement; “the refund tool is called before the confirmation message” is a process requirement; “the explanation is concise and empathetic” is a rubric criterion.
2. Keep deterministic checks close to the code
Use ordinary tests for parsing, schemas, tool interfaces, authorization, state transitions and other repeatable mechanics. Run generated-code tests and static analysis inside the evaluation run when the agent writes software. A failed code test should identify a concrete defect rather than being reduced to a vague quality score.
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Build cases from happy paths, edge cases, policy boundaries and, where appropriate, anonymized real traffic. Include ambiguous requests, missing data, tool errors, long conversations and adversarial instructions. A public benchmark score does not prove that the shipped application works for its own users; the dataset must represent your workflow.
4. Capture traces and outcomes
Retain model messages, tool calls and arguments, tool outputs, handoffs, retries and relevant environment state. The transcript explains how an agent failed. An independent outcome check establishes whether it actually completed the task. Never treat the assistant’s claim that an action succeeded as proof that the database or application state changed; inspect that state directly whenever possible. Anthropic’s guide describes an eval as giving an AI an input and applying grading logic to its output; for agents, the useful output includes the trace and the resulting environment state (Anthropic Engineering).
5. Match each criterion to a grader
Use exact assertions for objective conditions such as a status code, schema, file checksum or database row. Use code or heuristic evaluators for structured properties, such as whether a response contains a required citation or whether a tool argument is within an allow-list. Use an explicit rubric or calibrated judge for subjective qualities. Add multiple graders only when each answers a defined question; several vague scores create noise rather than coverage.
6. Repeat trials when stochasticity matters
Treat one task attempt as one trial. Run the same case multiple times when output variability could affect safety, cost or user experience, and record the model version, prompt, tools, temperature or other sampling settings, number of trials and aggregation method. Report distributions, pass rates or worst-case behavior as appropriate instead of presenting one favorable run as stable performance. Repetition is less important for a purely deterministic adapter test than for a multi-step task with several valid paths.
7. Compare versions and protect regressions
Run a stable regression set whenever you change the model, system prompt, tool definitions, retrieval settings or orchestration code. Keep exploratory capability tests separate from cases that are expected to pass consistently; otherwise a deliberately difficult experiment can make a release appear to regress. Compare versions on the same cases and preserve the traces behind meaningful score changes.
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8. Calibrate and update
Have people judge a sample of outputs and compare their decisions with the automated grader. Investigate disagreements: the rubric may be ambiguous, the judge may be biased toward a particular style, or the human label may be inconsistent. Monitor production for anomalies and convert representative, privacy-safe failures into new offline cases. This closes the loop between what you thought to test and what users actually encounter.
Designing the traditional layer for an agent
Test the boundary, not the model’s prose
Mock or fake the model when testing your application code. Assert that the orchestration layer validates structured output, handles a refused or malformed tool call, applies retry limits and records errors. The goal is to prove your code responds safely to each class of model behavior, not to predict every sentence the model might generate.
Verify tools independently
For every tool, test authentication, authorization, parameter validation, timeout and retry behavior, idempotency and error mapping. Contract tests should fail if a service changes a field or response shape. These checks make an agent eval easier to interpret: if a trajectory fails, you can distinguish a model decision from a broken adapter.
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After a mutating operation, verify the durable state. A booking test can check that exactly one reservation exists for the requested customer and time; a file-operation test can check the path, permissions and content hash. Include rollback and duplicate-request cases. An end-state assertion is more trustworthy than a final message saying “done.”
Designing the agent-evaluation layer
Represent tasks, not isolated prompts
Each case should contain the user request, available context, tool permissions, initial environment state and success criteria. Include expected constraints, such as “do not disclose another customer’s data” or “ask for confirmation before charging.” Store enough metadata to reproduce the run and to segment results by scenario.
Grade the trajectory when the route matters
Trajectory grading can check tool name, argument values, ordering, number of calls, handoffs, policy gates and recovery behavior. Permit multiple valid routes where the task allows them. A rigid “exact sequence” assertion can reject a safe alternative; define invariants and forbidden actions instead.
Grade the end state independently
Query the test environment after the run. Check records, files, permissions, messages sent or other durable effects with deterministic code. If the agent says it completed an action but the state is unchanged, mark the task as failed and retain the trace for diagnosis.
Use rubrics for language quality
Write observable criteria rather than asking whether an answer is “good.” For example: identifies the requested product, states the limitation, gives the next step, avoids unsupported promises and uses a professional tone. Define pass, partial and fail examples, then compare judge decisions with human labels before relying on the score in a release gate.
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How to handle nondeterminism without losing rigor
Variability does not make testing impossible; it changes what a useful result looks like. A deterministic test usually has a binary outcome for one input. An agent eval should state the trial count and summarize behavior across runs. Track at least:
- Task pass rate under the stated model, prompt and tool configuration.
- End-state failure rate, including harmful or unauthorized side effects.
- Trace-policy violations, such as forbidden tools or missing approvals.
- Distribution of latency, token or tool usage when those affect the product.
- Judge-versus-human disagreement for subjective criteria.
Set release thresholds according to risk. A low-risk drafting assistant may tolerate occasional wording variation, while an agent that changes financial or account state should require a consistently correct end state and zero unauthorized actions in the tested cases. Do not hide variability by averaging away a rare but severe failure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Offline regression and online monitoring solve different problems
Offline evaluation is controlled: the dataset, environment and grader are known, so versions can be compared before release. Online monitoring reveals distribution shift, new user intents, upstream outages and unexpected tool responses. Neither is sufficient alone.
Promote production incidents into the offline suite after removing sensitive data and defining a reproducible setup. Keep the original trace, the observed failure and the new expected behavior. Conversely, investigate offline failures that never appear in production; they may represent unrealistic cases, or they may be early warnings for a low-frequency high-impact problem.
Choosing an evaluation workflow or tool
Evaluate methods and platforms on seven practical axes:
- Determinism and repeatability: Can you fix the dataset, environment and run settings?
- Criterion fit: Does it test component correctness, workflow behavior or end-user outcome?
- Trace and state visibility: Can reviewers inspect intermediate decisions, tool arguments and environment state?
- Coverage of valid answers: Can it handle rubric-based quality instead of exact string matching?
- Cost, latency and throughput: Can the suite run often enough for your release cadence?
- CI and version comparison: Can results gate changes and compare model, prompt and tool versions?
- Production feedback: Can monitored anomalies become maintained evaluation cases?
Also check framework compatibility, data handling and product lifecycle. LangChain’s documentation describes offline benchmarks, unit and regression evaluations, historical backtesting, pairwise comparison and online anomaly monitoring, with LLM-as-judge and code evaluators (LangChain evaluation types). Its product page says the platform captures trajectories and intermediate decisions and integrates with pytest, Vitest and GitHub workflows (LangSmith product information). Those are vendor descriptions, not independent comparative tests; verify current features, pricing, data terms and framework fit for your implementation.
Separate the general practice of agent evaluation from any one hosted product. OpenAI’s current Evals documentation states that its platform will become read-only for existing users on October 31, 2026, and is scheduled to shut down on November 30, 2026. Those dates are time-sensitive, so confirm the documentation before making a migration or procurement decision.
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A worked example: a customer-support refund agent
Define the contract
Specify the allowed refund policy, required customer fields, approval boundary, confirmation wording and the exact account state that must exist after success. Mark which conditions are mandatory and which are preferences.
Build deterministic coverage
Unit-test field validation and policy calculations. Contract-test the refund adapter and authorization. Integration-test idempotency, timeout handling and the database transaction. After a successful run, assert that one authorized refund record exists and that a duplicate request does not create a second refund.
Add agent cases
Include an eligible request, an ineligible request, missing order information, a request requiring human approval, a tool timeout, a duplicate message and an attempt to reveal another customer’s data. Give each case an initial account state and permitted tools.
Grade three layers
- Trace: The agent selected the refund tool only after eligibility checks, supplied allowed parameters and escalated when approval was required.
- Outcome: The account state matches the policy and no unauthorized mutation occurred.
- Response: The message accurately states what happened, avoids claiming success when the state check failed and gives the next step when information is missing.
Run several trials for cases where the model may choose different valid routes. Keep a failed trace and the state snapshot together so an engineer can determine whether the defect was orchestration, tooling, policy handling or language quality.
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Common mistakes and their fixes
Replacing unit tests with a broad eval
Symptom: A score drops, but nobody can identify which parser, adapter or invariant failed. Fix: Preserve deterministic tests for every stable contract and use the eval to exercise their composition.
Checking only the final answer
Symptom: The assistant writes “completed” even though no record changed. Fix: Grade the durable end state independently and treat the response as a separate criterion.
Requiring one exact trajectory
Symptom: Safe alternative routes are marked wrong. Fix: Assert required and forbidden actions, permissions and outcomes; allow equivalent sequences.
Trusting an uncalibrated judge
Symptom: Scores improve while reviewers see no improvement, or a stylistic preference dominates correctness. Fix: Use a written rubric, label a sample with people, inspect disagreements and revise the grader.
Reporting one stochastic run
Symptom: A release passes once and fails in use. Fix: Repeat trials where variance matters, disclose the setup and aggregate results without concealing severe outliers.
Using a benchmark as a product guarantee
Symptom: A high external score does not predict failures in your tools, policies or users’ data. Fix: Maintain a task set drawn from your workflow and add sanitized production failures.
Bottom line: assign each question to the test that can answer it
Keep traditional tests for deterministic mechanics and invariants. Add agent evals for representative, multi-step behavior; inspect traces when the process matters and verify the real end state when side effects matter. Use calibrated rubrics for qualities that have several acceptable answers, repeat trials when outputs vary, compare versions on a maintained regression set and feed production failures back into that set. This layered approach gives engineers fast, precise failure signals without pretending that a probabilistic agent can be certified by assertions alone.
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