Build repeatable tests for AI-assisted development by separating exact software checks from evaluations of probabilistic model or agent behavior, then controlling and recording the inputs that can change each result. Run deterministic tests on every code change; rerun behavioral evaluations whenever prompts, models, retrieval, tools, or orchestration change. Treat AI-generated tests as drafts until a person checks that each test reflects a real requirement and has a trustworthy pass/fail rule.
Start by deciding what kind of result you need to test
A program with a defined expected result can usually be tested with conventional automated checks. A model or agent may produce different valid answers to the same prompt, so its quality often needs to be judged against a scenario and rubric rather than a single exact string. Production systems commonly need both: deterministic checks around the model and behavioral evaluations of the model-enabled workflow.
| Approach | Best fit | What counts as a pass | Main limitation |
|---|---|---|---|
| Deterministic software tests | Exact application logic, data preparation, permissions, input validation, and output processing | A specified result or invariant holds, such as a returned value, error, or denied access | They do not establish that a model’s open-ended response is useful, safe, or factually sound |
| Behavioral evaluation | Generative responses, agent decisions, and tool-using workflows | Scenario outcomes meet a stated rubric or threshold, with failures available for review | Results may vary between runs, and rubric-based grading is less exact than a deterministic assertion |
ISO/IEC TR 29119-11:2020 identifies non-determinism and the “test oracle problem” as central challenges in testing AI systems: it can be difficult to define one correct answer against which every output can be checked. That is a reason to make expected behavior explicit, not to abandon testing.
Specify behavior before asking an assistant to draft tests
Write down the requirement and acceptance criteria first. If the desired behavior is vague, an AI assistant can produce plausible-looking tests that encode its own interpretation rather than the product requirement. Specify observable outcomes, including what must happen on failure, before generating a test plan or code.
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Build a test matrix around risks
Ask for a matrix of candidate cases, then approve and adapt the cases yourself. Include:
- Happy paths: ordinary supported inputs and expected outcomes.
- Boundaries: empty, unusually large, malformed, or just-at-the-limit inputs.
- Negative cases: invalid requests and explicit error handling.
- Permissions: allowed and denied actions for relevant roles.
- Failure recovery: timeouts, unavailable dependencies, retries, and partial results.
- Security abuse cases: attempts to bypass controls, expose data, or induce unsafe actions.
For each approved case, record the requirement it covers, its inputs, the expected observable behavior, and why that behavior is correct. Prefer a precise assertion for exact logic; use a rubric only where acceptable model behavior cannot be captured by one exact value.
Make deterministic checks genuinely repeatable
A test is repeatable only when the conditions that influence its result are controlled or captured. AWS guidance on reproducible builds says that “Every build for a specific version of source code should ideally be able to generate the same outputs from the same inputs.” The same principle applies to test runs: a failure is much easier to diagnose when a rerun can use the same code, dependencies, environment, and fixtures.
Control the test environment and external effects
- Recreate the environment with containers or infrastructure as code, and record the environment definition.
- Pin dependencies and preserve lockfiles and runtime or tool versions so an installation does not silently drift.
- Replace third-party services with controlled mocks or fixtures when the test is intended to verify your own code rather than the vendor’s live service.
- Freeze or inject clocks where time affects behavior; control random generators with a recorded seed when supported.
- Restrict uncontrolled network access. If a test must call an external service, capture the dependency and its version or configuration as part of the run record.
These controls are especially important around AI boundaries. Keep deterministic coverage for code that prepares data sent to a model and for code that validates, authorizes, parses, or processes its output. A variable model response should not make those surrounding contracts untestable.
Evaluate model and agent behavior with scenarios and rubrics
For generative behavior, create a fixed regression set of representative scenarios and add newly sampled cases to broaden coverage. The fixed set reveals whether a change has damaged known behavior; new cases help uncover gaps the original set did not anticipate. Run the same scenario set again after a change to a prompt, model, retrieved context, tool, or orchestration logic.
Define grading criteria before running the evaluation
Set out what a reviewer or evaluator should judge, such as factuality, relevance, policy and safety compliance, correct tool use, and appropriate refusal behavior. Describe observable evidence for each criterion and decide how failures are handled. If using a score threshold, document the threshold and route failures for human review; a single aggregate score should not conceal a critical safety or security failure.
Do not assume repeated runs will produce identical outputs. Where behavior varies, record the scenario, run, and outcome, and assess whether the set of results meets the rubric and review gates. A seed can help reproduce a run only if the model or system supports it; it does not guarantee identical behavior across model versions or changing services.
Review AI-generated tests before relying on them
Generated tests can speed up discovery of edge cases, but generated code is not evidence that a requirement has been tested correctly. Review every adopted test for whether it checks the intended requirement, whether its expected result is justified, and whether it can fail for the right reason.
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- Check that expected outputs are not copied from the implementation in a way that repeats the same mistake.
- Verify fixtures, mocks, permissions, and error paths accurately represent the intended behavior.
- Review security implications, especially tests involving sensitive data, tool access, or model instructions.
- Remove brittle assertions that depend on incidental wording, ordering, timing, or hidden environment state.
- Keep the test maintainable and tied to a documented acceptance criterion.
Automate the repeatable path in CI/CD
Run deterministic tests on every relevant change and make deterministic regressions fail the pipeline. Run behavioral evaluations when the prompt, model, retrieval, tools, or orchestration changes; define score thresholds and human-review gates for those results rather than treating a probabilistic score as an exact software assertion.
Rank #4
Microsoft’s Copilot Studio documentation describes evaluations that can be run through REST APIs or connectors and integrated into CI/CD workflows. The practical objective is to rerun the same evaluation set as changes are introduced, while preserving results so a reviewer can understand why a gate passed or failed.
- Version the test cases and evaluation rubric with the code or configuration they govern.
- Configure CI to install the pinned dependencies and recreate the controlled environment.
- Run exact unit, integration, static-analysis, and other required checks on each change.
- Trigger the behavioral evaluation for changes that can alter model-enabled behavior.
- Fail automatically on deterministic regressions; route rubric failures and threshold breaches through the documented review process.
- Retain the run record and reports with the change so failures can be investigated and compared.
Layer quality, security, and safety checks
Do not collapse security, safety, and functional quality into one score. Cyber.gov.au recommends repeatable, scalable security testing across peer review, code review, unit and integration testing, static application security testing (SAST), dynamic application security testing (DAST), and software composition analysis (SCA). Use the checks appropriate to the system, alongside model-behavior evaluation.
Place high-value deterministic tests at boundaries where the application can enforce clear rules: access control, input handling, data minimization, tool permissions, output validation, and error recovery. Behavioral scenarios can then test whether the full AI-enabled workflow behaves acceptably under realistic and abusive situations. Each layer answers a different question, so passing one does not stand in for the others.
Best Value
Keep enough evidence to reproduce and audit a result
Store a run record alongside the relevant change. At minimum, preserve:
- Source revision, environment manifest, dependency locks, and relevant tool versions.
- Prompt and retrieved context versions, model identifier, and model settings.
- Tool configuration, test data, fixtures, and seeds where supported.
- Scenario set, expected outputs or grading rubric, and any applicable score threshold.
- Logs, evaluation reports, failures, and the result of any human review.
This record makes a test result explainable: a team can identify what was evaluated, under which conditions, and against what acceptance rule. The UK Home Office developer-testing standard states, “You MUST make tests repeatable.” Repeatability is therefore both a quality practice and a way to preserve an evidence trail for changes to AI-enabled systems.
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