Your tests live with your code, committed to the same repo. Every AI agent now sees the code and the tests that prove it, session after session. Testing visibility that compounds across agentic runs.
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Why GTMS
AI writes code fast. GTMS anchors it to your intent.
before your AI writes code
A test case sits in your repo, visible to every AI session. An anchor the AI can’t silently move.
during the AI coding loop
CI tells you the tests passed. GTMS tells you the code still matches your intent, even when the AI wrote both the code and the tests.
after the AI commits
A test case, an executable test script, a recorded result. All added to your regression pack as a by-product of normal work.
When AI can implement more features than anyone can carefully review, the developer’s job moves from writing every line to anchoring what matters, catching what drifts, and locking in what should never break again.
AI makes test creation cheap. GTMS makes tester judgement scale.
before a single test runs
AI drafts them using your guides and templates. Your structure, your standards. You apply the judgement.
while the testing runs
Your AI agent drives the app. GTMS gives it the case to follow and records the result. No selectors, no script.
after the test proves itself
When a test earns its place, graduate it to a scripted test. Same case, same dashboard row, no rewrite.
When AI can generate more tests than anyone can sensibly review, the tester’s value moves from authoring every step to deciding what matters, what passes, and what earns a place in the regression pack.
Proof
GTMS runs on GTMS.
GTMS built its own 2,000+ regression test pack.
AI is already capable of running plain-English tests.
And with every model release, that capability is only going to get better.
Go agentic or script it. GTMS lets you balance both.
How GTMS works
Follow the pipeline ↓
Point GTMS at a requirement and an AI adapter writes full, human-readable test cases with real intent, not the throwaway one-liners AI usually emits. GTMS owns the deterministic part: unique IDs, validation, and a traceable link back to the ticket, committed to your repo beside the code it tests.
Bring it as a file or fetch it live from your tracker. Jira today; PDF, Azure DevOps, or your own adapter next.
Hand the test case to an AI agent. It drives a real browser (Chrome CDP + Playwright MCP), exercises the feature, and records a structured pass, fail, or skip. Or run the test yourself and record the result. Agent or human, same path.
No automation written yet. You get coverage today, on the tests you haven’t automated yet. Exactly where manual QA lives.
When a test needs a deterministic script, graduate it. An AI agent writes it from the same intent, in whatever framework you run (Playwright here); GTMS wires it to the test case and keeps the lineage. Same test case, same history.
GTMS and the agent delivered a passing script fast. Now your coverage is auditable, reproducible regression.
A passing test can still be the wrong test. GTMS locks the human-readable test case up front and ties every script back to it. Then an independent AI reviewer (a tuned prompt) checks each script actually honours what the case asked. GTMS owns the deterministic linkage; the reviewer owns the judgement, and that split is the point.
It catches the drift CI happily went green on. Passing isn’t the goal. Honouring test case intent is.
One command, the whole picture. Every test case across every feature and bug-fix, with its state: passing, failing, primed, or a gap. gtms status shows the lot; gtms gaps shows what’s untested or failing.
No spreadsheet, no dashboard to maintain. It reads straight from your repo. Results live in git, so they aggregate across every AI session and travel with your code.
Join our weekly demo and see GTMS in action live.
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