GoKawiilSentinel: an open-source QA agent that reads your code before it clicks anything July 16, 2026
Sentinel reads the codebase, works out the real business flows, and tests them end to end across the frontend and the backend. It's open source under MIT.
The gap between clicking and understanding
Put a typical AI agent on your app and it opens a page, clicks a few buttons, notices a misaligned element or a console error, and calls the run done. That's useful the way a smoke test is useful. The agent has no model of what your product actually does.
A QA engineer worth hiring doesn't click around. They learn the product first, then reason about it: this is a hotel system, so I need to test a single booking, a group booking, a cancellation that frees the room back up, check-in, check-out, and the night audit, and I need to confirm each one actually persisted on the server instead of trusting that the UI looked happy.
What happened when we gave it a real app and no instructions
We pointed Sentinel at a working full-stack hotel PMS (a property management system): a Next.js frontend, a separate API service, a Postgres database. The PMS is KaribuKit, our own product, which is why we could hand an agent admin credentials for a disposable test tenant. That's also all it got — the repo and those credentials. No test plan, no list of flows.
It read the code, concluded the product was a boutique and safari hotel PMS, and derived nine critical business flows on its own: the full reservation lifecycle, group bookings, the lead-to-proposal pipeline, rate management, guest self-service, mid-stay room changes, the night audit, payment through invoice to refund, and the AI copilot. Cancellations are in there too, though this run folded them in as edge cases and backend checks inside the other flows rather than deriving a standalone flow. That's close to the list a human QA lead would write on day one, and nobody handed it to the agent.
The run itself: plan derived from the repo, top two flows deep-tested twice each, then the vision pass over every screen it visited.
Then it ran the top two of those flows, twice each, and the trace read like watching a person work:
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