AI Quality
2026.08.12
5 min
Testing Agentic AI: Why Output Accuracy Is Not Enough
Learn how to test AI agents using outcome, trajectory, tool-contract, policy, resilience, and cost evaluations instead of relying on final-answer accuracy.
Long-form writing on quality engineering, automation, CI/CD, and the moving target of AI inside the test lifecycle.
Learn how to test AI agents using outcome, trajectory, tool-contract, policy, resilience, and cost evaluations instead of relying on final-answer accuracy.
Learn how to convert traces, incidents, user journeys, and operational signals into privacy-safe regression tests and continuously updated QA priorities.
Learn how to connect an LLM to Playwright to classify failed tests and propose locator fixes without giving the model write access to your test suite.
Replace vanity coverage targets with a practical product-risk matrix that guides automation, exploratory testing, release gates, and ownership.
Five practical quality-gate patterns for GitHub Actions and Azure DevOps, plus two anti-patterns that create delay without reducing release risk.
Learn what replaces oversized page objects in a large monorepo, how to assign test ownership, and how to keep pull-request feedback under ten minutes.
Use this prompt and QA review checklist to inspect AI-assisted Playwright pull requests for false confidence, weak assertions, flaky patterns, and maintainability risks.
Book a private session and we'll work through it on your repo.