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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsBuild repeatable tests for AI-assisted development by separating exact software checks from evaluations of probabilistic model or agent behavior. Control the environment and inputs, turn approved test cases and grading rules into versioned artifacts, and run them through CI. Treat AI-generated tests as drafts: a person must verify that each test reflects a real requirement and has a trustworthy way to determine whether it passes.
Decide what kind of result you need to test
A code change produced with AI is not a special kind of software at runtime: test its specified behavior with the same disciplined checks used for other code. But an AI model or agent may respond differently to the same prompt, so evaluating its behavior requires scenarios, explicit grading criteria, and repeated runs—not just assertions against one exact output. Most production systems need both kinds of evidence.
| Approach | Best suited to | What a pass means | Main limitation |
|---|---|---|---|
| Deterministic software tests | Exact logic, interfaces, permissions, data transformations, and failure handling | The observed result matches a defined expected result for controlled inputs | Cannot, by themselves, establish that variable generated responses are useful or safe |
| Behavioral evaluation | Model or agent responses, tool use, refusal behavior, and other probabilistic outcomes | Results across a defined set of scenarios meet a documented rubric or review gate | Scores depend on the scenarios, rubric, evaluator, and run conditions; a score is not a guarantee of correctness |
ISO/IEC TR 29119-11:2020 identifies non-determinism and the test-oracle problem—deciding whether an AI system’s result is correct—as central challenges in testing AI systems. That is why a single “AI test” should not be expected to prove both exact code correctness and open-ended response quality.
Write the specification before asking AI to write tests
Start with observable requirements: the input, expected behavior, relevant boundaries, and what should happen when something fails. A test is only as useful as its oracle—the rule or evidence used to decide whether the result is acceptable. If the requirement is vague, an assistant can produce plausible-looking tests that encode its own assumptions instead of the intended behavior.
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- Record acceptance criteria. State outcomes in terms that a test or reviewer can observe, including constraints and failure behavior.
- Ask for a test matrix, not just test code. Request happy paths, boundary cases, invalid inputs, permissions, recovery behavior, and security-abuse cases. Require a short explanation of which acceptance criterion each case covers.
- Review the matrix against the specification. Remove duplicate or irrelevant cases, add missing risks, and resolve ambiguous expected results before implementation.
- Approve the oracle. Decide whether each case should use an exact expected value, a property or invariant, a security rule, or a rubric judged by a person or evaluator.
For example, if an application summarizes a document, exact tests can verify that the right document is supplied, access permissions are enforced, and output is stored or handled correctly. Whether the summary is relevant or factually grounded is a behavioral evaluation question and needs its own criteria.
Control the inputs that can change a result
Repeatability is an evidence property: another run should have the information and conditions needed to reproduce or explain the result. AWS guidance says each build for a specific source version should ideally generate the same outputs from the same inputs. In practice, record and control the factors that can move a test outcome:
- Environment: use a reproducible container or infrastructure-as-code definition; record operating-system, runtime, and tool versions.
- Dependencies: pin and lock packages, and retain the lockfile with the change.
- Time and randomness: freeze clocks and control random generators in deterministic tests. Record seeds where supported; a seed may not make a remote model response identical.
- Network and external services: mock third-party APIs for ordinary deterministic tests, and restrict uncontrolled network access. If an external service is part of the behavior being evaluated, record its identity and relevant configuration and treat its changes as a possible source of variation.
- AI configuration: save the prompt, model and version identifier when available, tool settings, orchestration configuration, retrieved context, and test data.
- Run evidence: retain logs, environment manifests, reports, and outputs needed to diagnose a failure.
These controls improve the chance of reproducing a result; they do not guarantee identical output from every hosted model or changing external service. When exact replay is unavailable, preserve enough configuration and run evidence to understand what changed.
Convert approved cases into two complementary test layers
Keep deterministic checks on code boundaries
Use conventional unit and integration tests for behaviors with exact expected outcomes. Add static analysis, security checks, and performance tests where their results can be specified and meaningfully measured. Pay particular attention to the code that prepares data for a model and the code that validates, authorizes, transforms, or processes its output. These are ordinary software boundaries where deterministic coverage can catch defects even when the model response varies.
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Use fixtures for stable inputs, and replace external services with controlled doubles when the test is not meant to assess the external service itself. Keep the expected behavior tied to the requirement, not to whatever output an assistant happened to generate while drafting the test.
Use a rubric and scenarios for variable behavior
For model or agent behavior, keep a fixed regression set and add newly sampled cases to expose gaps beyond that set. Write a rubric before running evaluations. Depending on the task, assess factuality, relevance, policy and safety compliance, correct tool use, and appropriate refusal behavior. Define what evidence counts for each criterion and how failures are reviewed.
Run multiple trials when variation itself matters, and report the scenario set, configuration, number or pattern of runs, rubric, and results together. A behavioral score is meaningful only in the context of those choices; it should not be presented as an absolute measure of quality or safety. Set any score threshold or human-review gate for the application’s risk, rather than borrowing a universal cutoff that does not exist.
Review AI-generated tests before relying on them
Generated tests can accelerate case discovery and drafting, but they can also test an implementation detail, assert an incorrect expectation, miss a security implication, or be too brittle to maintain. Before merging, a reviewer should confirm:
- The test traces to an approved requirement or risk.
- The expected result is correct and independent of the code under test.
- The case covers a meaningful boundary, failure mode, or behavior rather than duplicating existing coverage.
- Mocks and fixtures isolate only the dependencies intended to be controlled.
- The test does not expose secrets, weaken access controls, or normalize unsafe behavior.
- The test remains understandable and maintainable without relying on hidden assumptions from the AI conversation.
Store approved tests as ordinary version-controlled project artifacts. Store prompts and evaluation configuration alongside them where practical, so reviewers can see how AI assistance shaped the proposal without treating the generated test as authoritative.
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Make the test path repeatable in CI
Automate the same approved test sets and configuration so changes can be compared consistently. Microsoft documents that agent evaluations can run through REST APIs or connectors and be integrated into CI/CD workflows. The specific integration depends on the platform, but the control design is broadly the same:
- Run deterministic tests on each relevant change. Include unit, integration, static-analysis, and applicable security checks in the normal pipeline. Fail the build on deterministic regressions.
- Trigger behavioral evaluations on behavior-changing edits. Run the fixed evaluation set when prompts, models, retrieval, tools, or orchestration change. Record which change triggered the run.
- Define gates before seeing results. Specify which deterministic failures block a merge, which behavioral results require a human review, and any team-approved score threshold. Do not silently change the gate after an unfavorable result.
- Publish results with the build. Attach test reports and relevant logs, version identifiers, environment details, and evaluation settings to the change or pipeline record.
- Investigate rather than suppress flakes. Determine whether variation came from uncontrolled test inputs, a changing dependency, a genuine product regression, or expected model variability. Fix the source or make the behavioral gate account for the documented variation; do not simply retry until a passing result appears.
This separation lets CI treat a failed exact assertion differently from a borderline behavioral score. A deterministic regression can be a direct failure; a statistical or rubric-based result may call for a pre-defined threshold, human review, or both.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Layer security and quality instead of collapsing them into one score
Security and quality checks answer different questions, so an aggregate evaluation score should not replace them. Cyber.gov.au recommends repeatable, scalable security testing that includes peer review, code review, unit and integration testing, static application security testing (SAST), dynamic application security testing (DAST), and software composition analysis (SCA). Apply the checks appropriate to the system and keep them visible as separate evidence.
Best Value
For AI-assisted changes, combine those practices with deterministic checks around model inputs and outputs, plus behavioral scenarios for misuse, unsafe requests, permissions, and refusal behavior. A strong result in one layer does not compensate for a failure in another.
What to keep with each change
A compact, versioned record makes a test result useful beyond the machine that ran it. Preserve the artifacts needed to rerun, compare, or investigate:
- Acceptance criteria and approved test matrix
- Test code, fixtures, dependency locks, and environment or infrastructure definitions
- Prompts, model/version identifiers, retrieval context, tool settings, and supported seeds
- Behavioral rubric, evaluation scenarios, thresholds, and review decisions
- CI logs, reports, outputs, and relevant service/configuration identifiers
The record should distinguish what was checked exactly from what was evaluated against a rubric. That distinction makes failures easier to reproduce and prevents a probabilistic score from being mistaken for proof that every code path is correct.
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