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Use both. Traditional unit and integration tests should enforce deterministic contracts such as schemas, parsing, tool adapters, state transitions, database invariants and generated-code compilation. Agent evaluations should exercise the model-plus-harness on representative tasks, grading tool choices, handoffs, policy behavior, multi-turn context and the resulting state or answer.
An eval is not a larger unit test, and a unit test is not a substitute for an end-to-end agent trial. Build a layered system: fast deterministic tests close to the code, task-level evals for workflow behavior, objective checks against the real environment, and calibrated rubrics for qualities that cannot be reduced to an exact value.
What traditional software tests measure
Unit tests: one component, one contract
A unit test isolates a function or class and asserts a repeatable result. For an agent application, that might be token-budget handling, JSON parsing, schema validation, a permission check, a prompt-rendering function, or a tool adapter that converts model arguments into an API request. The input and expected output are controlled, so a failure normally points to a small area of code.
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Integration tests: components working together
Integration tests connect several known components: an agent service to a tool server, a database transaction to an event publisher, or a retrieval layer to a response formatter. They can run with fakes or a controlled test environment and still assert exact properties such as an HTTP status, emitted event, database invariant or serialized payload.
For generated code, run the project’s compiler, linter and test suite as ordinary checks. The model may produce different text on each run, but compilation and the project’s tests provide an objective acceptance boundary.
What these tests cannot establish
A green unit suite does not show that an agent will select the correct tool, recover from an API error, preserve instructions across turns, or complete a multi-step task. Integration tests can prove that a tool adapter works when called with a prepared payload; they do not prove that the model will choose that adapter, supply safe parameters and stop at the right point.
What an LLM agent evaluation measures
Anthropic defines an evaluation as a test in which an AI receives an input and grading logic measures success. Its agent-evaluation guide treats an attempt as a trial: the same task can produce different trajectories because model output is variable.
Task and workflow behavior
An agent eval starts with a realistic task, not an isolated function. It can record the full transcript, tool calls, arguments, tool results, handoffs, retries and final response. Graders then ask questions such as:
- Did the agent identify the user’s goal and required constraints?
- Did it call the allowed tool, with valid parameters, in the correct order?
- Did it hand off to a human or specialist when policy required it?
- Did it maintain relevant context over multiple turns?
- Did it finish within limits for time, steps, permissions and cost?
Outcome rather than assertion
The final message is only one observation. An agent can say that a refund was issued while the payment system shows no refund, or claim that a file was updated when the file is unchanged. Grade the end state in the environment whenever you can: inspect the database, booking record, filesystem, ticket status or generated artifact. Trace grading explains how the agent behaved; state grading establishes whether the requested change actually happened.
Quality with multiple acceptable answers
Natural-language tasks often have several valid answers. Exact string matching is then brittle. Use a rubric that names observable criteria such as factual grounding, completeness, clarity, policy compliance and appropriate uncertainty. A code evaluator or heuristic can grade structured properties; a language-model judge can score a rubric; people should review samples and disagreements so the automated judge remains calibrated. LangChain’s documentation describes code evaluators, LLM-as-judge methods, pairwise comparison and online monitoring as separate evaluation types; these are capabilities documented by the vendor, not independent performance tests. See the evaluation-types documentation.
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Which method to use for each need
| Need | Prefer | Example check |
|---|---|---|
| A deterministic component contract | 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 or policy adherence | Trace or trajectory grading | Was the permitted 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 occur 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 do production anomalies become new evaluation cases? |
The practical answer to “Do I need evals if I already have unit tests?” is yes whenever the product promise is a task completed by a model-driven workflow. The practical answer to “Should evals replace unit tests?” is no: retain exact tests for every behavior that can be specified without a model.
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Build a combined testing and evaluation loop
1. Define success in observable terms
Write each task as an input, an environment setup and explicit success criteria. Separate three categories:
- Required end state: the record, artifact or external side effect that must exist after completion.
- Process constraints: tools, permissions, handoffs, approval gates, step limits or data-access rules that must be respected.
- Quality criteria: properties such as clarity, tone, grounding, completeness or useful explanation.
This separation prevents a polished final answer from masking a failed side effect and prevents a preferred trajectory from being treated as the only valid one.
2. Keep deterministic checks close to the code
Use ordinary tests for parsing, schemas, tool interfaces, authorization, retries, state invariants and serialization. Put them in the same CI jobs as the implementation so a small change fails quickly and locally. For coding agents, execute static analysis and the generated project’s own tests rather than asking a grader to infer whether the code works.
3. Assemble a representative task set
Start with happy paths, then add edge cases, malformed inputs, ambiguous requests, policy boundaries, tool outages, permission denials and long multi-turn conversations. Include cases drawn from real user traffic when privacy and consent allow it. A public benchmark score does not establish that an agent works for your own tools, policies and data; your dataset must represent the workflow you ship.
Keep exploratory capability probes separate from release-regression cases. Exploratory cases discover what a model can do and may have no single expected trajectory. Regression cases are maintained examples that should pass a defined threshold consistently enough to protect users from regressions.
4. Capture traces and environment state
For each attempt, retain the task identifier, model and prompt version, tool calls and arguments, tool outputs, handoffs, timing, token or cost measurements when relevant, final response and the relevant environment state before and after the run. Redact secrets and personal data according to your retention policy.
A transcript is useful for diagnosis, but it is not proof of completion. Pair trace records with a state snapshot or query that can determine whether the requested effect occurred.
5. Match each criterion to a grader
Use the least subjective grader that answers the question:
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- Exact assertion: status codes, schema validity, required fields, numeric limits and state invariants.
- Code or heuristic evaluator: static-analysis findings, forbidden phrases, citation presence, tool-parameter shape or step counts.
- Trajectory grader: tool choice, call order, handoff, policy adherence and recovery behavior.
- End-state grader: a query or comparison against the real application state or generated artifact.
- Rubric or judge: groundedness, clarity, completeness, tone or code quality when several answers are acceptable.
- Human review: calibration samples, disputed cases, new failure modes and high-impact decisions.
Add multiple graders only when each answers a distinct question. Five overlapping judges create noise and expense without improving diagnosis.
6. Repeat trials when stochasticity matters
Generative systems can produce different outputs from the same input. OpenAI’s evaluation best-practices guide explicitly notes that this variability makes traditional software-testing methods insufficient for AI architectures. Run repeated trials for tasks where reliability matters, and report the trial count, model and prompt versions, pass definition and aggregation method.
Useful summaries include the proportion of trials meeting all required conditions, separate rates for process and end-state failures, and the distribution of quality scores. A single successful run is evidence that the task is possible, not that the workflow is dependable.
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Run the maintained suite whenever you change a model, system prompt, tool schema, retrieval index, safety policy or orchestration code. Compare versions on the same cases and inspect both wins and losses. A change that raises answer quality while increasing unauthorized tool calls is not an unqualified improvement.
8. Calibrate and update
Sample judge decisions and compare them with human labels. Investigate systematic disagreement, ambiguous rubric language and cases where the judge rewards a plausible explanation despite a wrong state. Monitor production traces and anomalies, then promote representative failures into the offline suite after removing sensitive data and documenting the expected behavior.
A layered architecture for agent quality
Layer 1: fast code contracts
Run unit tests on every change. Cover deterministic transformations, validation, authorization, tool adapters and state-machine transitions. These tests should identify defects before any model call.
Layer 2: controlled integrations
Exercise tool servers, databases, queues and retrieval components in a controlled environment. Verify timeout, retry and partial-failure behavior. Keep fixtures stable so a failure is attributable to the integration rather than an unrecorded external change.
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Run realistic tasks through the complete agent harness. Grade traces, intermediate decisions and final responses. Include multi-turn context and adversarial or ambiguous inputs that ordinary component tests cannot express.
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Layer 4: environment outcomes
Query the system of record after each task. For a support workflow, check ticket status and audit events; for a booking workflow, check the reservation; for a coding workflow, compile and run tests on the produced artifact. Make outcome failure independent of the agent’s self-report.
Layer 5: production monitoring
Measure live success signals, anomaly rates, tool errors, escalation frequency and user corrections within your privacy constraints. Production monitoring finds distribution shifts that a static dataset cannot. It does not replace pre-release regression runs; it supplies new cases and early warning.
Worked example: a refund-support agent
Deterministic tests
Unit-test currency parsing, refund-limit calculations, customer-identity checks and the payment-tool adapter. Integration-test that an approved refund creates the expected ledger entry and emits an audit event. These checks do not require a language model.
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Agent eval task
Provide a conversation in which a verified customer requests a refund for an eligible purchase. The success criteria require the agent to retrieve the order, confirm eligibility, request approval when the amount exceeds its limit, call the refund tool exactly once after approval, and explain the result without exposing internal data.
Graders and failure diagnosis
- The trace grader checks that identity verification preceded the order lookup and that an approval handoff occurred for an over-limit amount.
- The end-state grader checks the payment ledger and ticket status rather than trusting the final message.
- A rubric grader assesses whether the explanation is accurate, clear and appropriately bounded.
- Repeated trials reveal whether the agent sometimes skips verification or invents a refund after a tool timeout.
A failed ledger check is an outcome defect even if the response sounds correct. A correct ledger with an unauthorized tool call is a process and policy defect. Keeping these results separate tells the team what to fix.
Evaluating coding and artifact-producing agents
For a coding agent, combine agent-level evaluation with the repository’s normal quality gates. The task eval can grade whether the agent inspected the relevant files, selected permitted tools, respected repository instructions, explained trade-offs and stopped after implementing the requested change. The artifact checks should compile the code, run unit and integration tests, execute static analysis and verify generated files against required schemas.
When several implementations satisfy the tests, use a rubric for maintainability, clarity, security and scope control. Do not let a language-model judge replace executable tests when the repository can establish correctness directly.
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Comparison criteria
Assess a method or platform on seven axes:
- Determinism and repeatability of its runs.
- Whether it measures component correctness, workflow behavior or end-user outcome.
- Access to intermediate traces and environment state.
- Coverage of multiple valid answers and subjective qualities.
- Cost, latency and throughput at your required trial count.
- Ease of CI integration and version-to-version comparison.
- How production failures are discovered, redacted and promoted into maintained cases.
For a platform, also check framework compatibility, data handling, retention controls and product lifecycle. Do not treat a vendor’s benchmark score or feature list as evidence that your task-specific design is sound.
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Documented workflow examples
OpenAI’s agent guidance describes grading traces for tool-choice, handoff, policy and end-to-end workflow problems, followed by datasets and repeatable evaluation runs for comparisons. See Evaluate agent workflows for that workflow. OpenAI’s general evaluation guidance covers dataset-based and grader-driven practices at Evaluation best practices.
OpenAI’s current Working with evals documentation states that its Evals 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; verify the page before committing to a vendor-specific workflow. The general practice of designing evaluations remains separate from the lifecycle of that product surface.
LangSmith documentation describes offline benchmarks, unit and regression evaluations, historical backtesting, pairwise comparison and online anomaly monitoring. Its product page says it captures trajectories and intermediate decisions and integrates with pytest, Vitest and GitHub workflows. These statements describe the vendor’s documented capabilities, not an independent comparison. Review current features, pricing, data terms and framework fit at the LangSmith product page and the evaluation-types documentation before adoption.
Common mistakes and how to correct them
Replacing assertions with a single judge score
A composite score can hide a critical failure such as an unauthorized transfer or missing database update. Keep hard safety and state requirements as pass/fail checks, and report subjective quality separately.
Checking only the final answer
Inspect traces and the environment. A confident response can conceal a wrong tool, skipped approval or failed side effect.
Assuming one run represents reliability
Repeat trials for variable tasks and publish the aggregation method. Distinguish capability discovery from a release threshold.
Using an unrepresentative dataset
Include edge cases, policy boundaries, outages, ambiguous language and real traffic patterns. Refresh the set when production reveals a new failure class.
Over-constraining the trajectory
Require a specific call sequence only when policy or safety demands it. Otherwise grade the necessary outcome and constraints; multiple safe paths may be valid.
Ignoring grader drift
Model judges can change behavior when you change the judge model, rubric prompt or context formatting. Version graders, retain calibration examples and rerun human agreement checks after changes.
Forgetting operational limits
Record timeout, step, permission, latency and cost limits as part of the task definition. An answer that is correct but exceeds a user-visible deadline or calls a forbidden service is not a passing production behavior.
Quick Recap
Release checklist
- Every deterministic parser, schema, adapter and invariant has a conventional test.
- Each agent task states its required end state, process constraints and quality criteria.
- The dataset contains happy paths, edge cases, policy boundaries and representative real-world examples.
- Traces include model and prompt versions, tool inputs and outputs, handoffs and relevant environment state.
- Objective checks run before subjective judges where possible.
- State-changing tasks are verified against the system of record.
- Repeated trials and aggregation are defined for stochastic tasks.
- Prompt, model, tool and architecture changes run against a stable regression set.
- Automated graders are calibrated against human judgments and disagreements are reviewed.
- Production anomalies have a documented path into the offline suite.
- Data retention, redaction, framework compatibility and vendor lifecycle are approved before selecting a platform.
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