Agentic AI test automation for web apps

Agentic AI test automation means an AI agent decides what to do at runtime against a stated goal, instead of replaying recorded steps or brittle CSS selectors. In TestAutomate, you describe each regression test's intent in plain English, an AI QA agent works out the steps on the live page in a real browser, and a separate verifier model grades the outcome against your expected result. When the UI changes but the workflow still works, the test still passes — and tells you what changed.

An AI QA agent is autonomous software that runs quality assurance the way a human tester would: it reads the page, plans the next action, adapts when the app changes, and reports what it found with evidence. It replaces the brittle middle of test automation — selector maintenance, step-by-step scripting, and the triage of red builds that were never real bugs — while humans keep the judgment calls: what to test, which drafts to approve, and which bugs are worth filing.

Yes — and more than codeless. Tests are written in plain English or dictated by voice: no scripts, no CSS or XPath selectors, no page objects to repair after a redesign. Each test is stored as intent — a prompt plus an expected outcome in portable, human-readable YAML — so engineers can diff and version-control everything while non-engineers author freely. Natural-language test automation only counts if no selector layer hides underneath the English; here, none does.

Self-healing test automation still stores element locators — it just repairs them after they break, and every repair is a guess that can quietly change what your test checks. Selectorless testing stores no locators at all: the agent locates elements at runtime the way a person does, by reading the page. There is nothing to heal because nothing brittle was saved. That is the difference between patching the old architecture and removing the failure mode.

Every run gets a graded verdict — pass, fail, or blocked — so a broken test environment can never masquerade as a product failure, and a renamed-but-equivalent UI passes with an advisory flag instead of a red build. No failure is reported until a stronger model re-drives the whole flow and agrees, which filters agent noise from real defects. And a pass that needed a retry is labeled "passed on retry", never laundered into green — flaky behaviour stays visible instead of hidden.

Yes — that is the point of driving your own Chrome instead of a vendor's cloud browser. The agent uses your real logged-in sessions, so staging behind a VPN, enterprise SSO like Okta, magic links, and TOTP-gated logins are all reachable without tunnels or IP allowlisting. For scheduled, unattended runs, named auth entries per app — email and password, Google, Okta, magic links, TOTP — are learned from real runs and kept encrypted per organisation, so login is solved once and reused by every test after.

Three sources, cross-checked: your documentation says what the product should do, your repository says what surfaces exist, and the running app says what is actually there. TestAutomate generates draft test cases from that reconciliation, so expectations come from your docs' stated intent instead of grading the implementation against itself. You review and approve every generated test case before anything runs, and you can author or dictate new tests in plain English at any time.

A confirmed failure becomes evidence first: screenshots, the run trajectory, and an itemized verdict against each expectation. Then it becomes a bug draft, deduplicated against your live tracker so known issues are not refiled — and nothing posts until a human confirms it. When the fix ships, the agent re-runs the flow and closes the verified issue back on your board. Sprint UAT works the same loop: your board query becomes a work queue, and every re-fix is re-verified.

On your own AI key, model spend passes through at cost — no markup — with a pre-run estimate, a live dollar meter, and a hard budget ceiling that pauses the run before overspend rather than after. No key? Managed credits show transparent math: provider price plus a stated fee. Developers can run locally on an existing coding-agent subscription with no extra model spend. And costs fall with use, because logins, navigation paths, and app quirks are learned once and reused by every later run.

Yes. The entire stack — Node and Postgres — can run inside your own infrastructure, and your suite exports to portable YAML in one click, so nothing you build is locked in. Credentials are sealed with AES-256-GCM, workspaces are tenant-isolated, and every write to your tracker is confirm-gated: the agent never files, comments, or transitions anything without a human approving it first.