Stop maintaining tests, start shipping features

A traditional assertion checks for an exact match, like assertEquals(expected, actual). That breaks the moment output is non-deterministic, which is the norm for any chatbot or AI agent. An AI Assertion replaces the exact match with a plain-English question, such as "Does the response acknowledge this term doesn't exist, rather than fabricating one?" The AI engine evaluates intent and meaning against that question, not exact wording, so it still passes when a response is phrased differently but means the same thing.

Yes. The same platform that creates and self-heals tests for web apps also tests conversational interfaces. AI Assertions evaluate a live exchange the way a real user experiences it, so you can check whether an agent stayed on brand, refused a jailbreak attempt, or held up several turns into a real conversation.

No. AI Assertions evaluate the rendered conversation through the browser, the same interface your users see. There's no need to hook into your agent's API, prompts, or model, so it works even when you don't own or can't access the underlying implementation.

AI Test Automation tests the conversational and browser layer of Harness Agent DLC: the development lifecycle for building, testing, storing, deploying, operating, and governing AI agents on one platform. Where AI Evals scores agent logic against datasets, AI Assertions validate what a real user actually sees and experiences, so you're never reconciling separate tools to know an agent is safe to ship.

Self-Healing Smart Selectors identify elements by context rather than fixed IDs or classes, so when the DOM changes, selectors adapt automatically instead of failing. AI Assertions add another layer of resilience on top: because they validate outcomes in plain language rather than brittle string matches, both the locators and the checks stay intact through UI changes.