Prove AI systems work
AI Testing & Evaluation
AI systems combine deterministic software with probabilistic models and autonomous tool use. Reliable testing therefore needs multiple layers: contract and workflow tests, representative evaluation datasets, agent traces, structured-output validation, human review, and production feedback. This guide connects those layers without turning AI evaluation into generic software QA.
Decisions this guide helps you make
- Which behavior should be a deterministic test and which requires an evaluation rubric.
- How to build datasets that represent real users, edge cases, and failure costs.
- How to validate agent tool calls, permissions, recovery, and human approval boundaries.
- How to gate model, prompt, retrieval, and workflow changes before production.
Testing AI applications
Test the deterministic application around the model as rigorously as any other production system, then add checks for streaming, tools, background work, and variable model output.
How to test AI applications
Build a practical test strategy across APIs, prompts, retrieval, tools, and user workflows.
Read guide →AI API testing with Postman
Test authentication, streaming, errors, latency, and response contracts around model endpoints.
Read guide →Test AI-generated code before shipping
Verify generated changes with deterministic checks, review boundaries, and deployment gates.
Read guide →Handle AI API failures
Exercise retries, fallbacks, partial responses, provider outages, and safe error behavior.
Read guide →Webhook architecture for AI workflows
Test asynchronous delivery, signatures, duplicate events, ordering, and background jobs.
Read guide →LLM evaluation
A passing HTTP request does not prove that an answer is correct, useful, grounded, or safe. Evaluate quality against explicit tasks and representative datasets.
AI model evaluation checklist
Define task-specific quality, safety, latency, and cost criteria before comparing models.
Read guide →Build an LLM evaluation dataset
Create representative examples, edge cases, expected behavior, and reviewed labels.
Read guide →LLM regression testing
Detect quality changes when prompts, retrieval, tools, or models change.
Read guide →LLM as judge
Use model-based scoring carefully, with calibrated rubrics and human validation.
Read guide →Evaluate models for your use case
Compare models on your real tasks instead of relying on a general leaderboard.
Read guide →Agent testing
Agents combine uncertain reasoning with consequential tool calls. Validate actions, permissions, state transitions, recovery, and approval—not only the final answer.
Evaluate AI agent reliability
Measure task completion, tool correctness, recovery, and repeatability across agent runs.
Read guide →AI agent governance framework
Connect test evidence with ownership, approvals, risk levels, and release decisions.
Read guide →Agent audit logging
Capture traces that explain which tools, permissions, inputs, and approvals produced an action.
Read guide →Debug AI agents
Diagnose planning, context, tool, state, and recovery failures systematically.
Read guide →Structured output validation
Treat model-generated JSON and tool arguments as untrusted input. Validate structure and semantics before the application changes state.
Structured outputs explained
Understand schemas, constrained generation, validation boundaries, and remaining failure modes.
Read guide →Zod parse error
Diagnose schema failures without silently accepting invalid model or tool output.
Read guide →Zod vs Yup
Choose a validation layer for typed application and model-response contracts.
Read guide →Invalid model JSON
Recover safely from truncated, malformed, or schema-incompatible responses.
Read guide →AI API design
Design streaming, tool-call, job, and response contracts that can be tested reliably.
Read guide →CI/CD for AI quality
Run deterministic tests first, then evaluations. Make regressions visible and require evidence before model, prompt, agent, or retrieval changes reach production.
CI/CD pipelines for AI applications
Gate releases with tests, evaluations, secret checks, previews, and deployment verification.
Read guide →GitHub controls for coding agents
Protect branches, require review, isolate credentials, and keep agent changes auditable.
Read guide →GitHub vs GitLab for AI teams
Choose the repository, runner, approval, and governance control plane.
Read guide →Canary deployments for LLM features
Release model and prompt changes to controlled traffic with rollback signals.
Read guide →Browser agents and automation
Browser agents interact with changing interfaces and external state. Test selectors, permissions, retries, confirmation steps, and recovery from partial execution.
Playwright vs Cypress
Compare browser testing foundations for AI applications and agent-driven workflows.
Read guide →Playwright timeout troubleshooting
Diagnose timing and state failures in browser automation and agent-driven tests.
Read guide →Generate E2E tests with AI and Playwright
Use AI to accelerate test creation while preserving deterministic assertions and review.
Read guide →AI code review vs AI testing
Understand what review can catch and what execution-based testing must prove.
Read guide →