AI-powered testing for web apps.
An agent explores your application the way a person would — clicking, typing, browsing — and turns what it finds into reproducible tests, regression alerts, and a map of the surfaces it can't reach.
Two pages returned 5xx during exploration. One blocking, one transient.
2 page types behind auth — sign-in fixture missing.
3 discovered but not visited before time budget elapsed.
- 500
/products/cinder-jacket - NAV
/checkout - 404
/help/returns - AUTH
/account/{*}
Point it at a URL. Get a working test suite.
Configure once.
Give Dagonet a URL, a sign-in fixture if needed, and a budget. Skip the assertion library, the page-object scaffolding, the brittle selectors.
An agent explores.
Dagonet drives a real browser. It clicks, types, navigates, tries personas, recovers from dead-ends. Every page, action, and DOM mutation is recorded.
Tests run, regressions surface.
What the agent learned becomes a generated test suite. Every subsequent run replays it and flags whatever started to fail — visually, behaviourally, or at the network layer.
The product is the proof.
Tests written from observation, not from a brittle DSL.
Every action the agent took becomes a reproducible test case. Selectors are stable, assertions match what actually rendered, and the suite grows with the app instead of rotting.
| Pattern | Status | Pages | Lifetime |
|---|---|---|---|
| /products/{slug} | Confirmed | 4 | 5,042 |
| /checkout | Errored | 1 | 1 |
| / | Confirmed | 1 | 1 |
| /help/{section} | Candidate | 2 | 2 |
| /account/{section} | Auth-blocked | 0 | 0 |
The first run that fails is the one you need to see.
Every subsequent run replays the suite and surfaces the rows that flipped from green to red — visually, behaviourally, or at the network layer. No noise from cosmetic diffs.
One app, three users, three explorations.
Dagonet runs the same crawl through different personas — first-time visitor, returning user, admin operator — and shows you which surfaces only one of them can reach.
reached
reached
reached
Other things Dagonet handles while it's there.
Reads the app as it renders.
The agent doesn't work from a static map. It sees the same DOM your users see, in the same browser, on every run.
Continuous, not on-demand.
Daily, hourly, on every deploy. Dagonet runs in the background and only interrupts you when something changes.
Every link, every page, every run.
404s, 500s, redirect loops, broken anchors. Tracked across runs so you can see the day a link started rotting.
AI ships code faster than teams can review it.
Cursor, Claude Code, Lovable and Bolt push features live before a human reads the diff. The QA pipeline built around weekly releases doesn't fit a deploy-on-every-commit world.
AI tools confidently emit code that compiles, runs, and contains bugs. Vibe-coding platforms compound it — code reaches users without anyone reading it.
mabl, Tricentis and similar platforms are priced for enterprises with dedicated QA teams. Most teams shipping AI-generated code do not have those resources.
Cursor, Claude Code, Lovable, Bolt.new and Bubble move applications to production in hours. Manual QA is now the bottleneck — and the existing tools are not priced for the teams who need them.
Built for teams shipping faster than they can test.
Agency heads & technical directors.
Delivering client web applications under fixed-price contracts. Catch regressions before the client does, without staffing a QA team for every project.
Startup CTOs & technical founders.
Shipping fast with three-person teams. The agent gives you the QA pass an enterprise QA team would do — without the headcount or the enterprise price tag.
Product & QA leads.
Drowning in manual regression. Reclaim the hours spent re-clicking the same flows every release and re-route them into exploratory work that only a human can do.
Sign up and join the waitlist.
Dagonet is a Cloud SaaS. Active early-access users get test runs free while we calibrate the agent against real targets and harden the platform.
Paid plans launch at general availability; pricing will be announced ahead of that date. Users on the early-access programme keep their free-tier benefits when paid plans open.
Join the waitlistBuilt by an engineer who got tired of manual QA.
Bryn is the technical founder of Dagonet. His background spans software engineering and applied AI — enough years writing tests by hand to know which parts of QA an autonomous agent can actually take over, and which parts a human still needs to judge.
Dagonet is operated by Anbaric Ideas Pty Ltd, registered in New South Wales, Australia. The platform is deployed continuously from a single mainline branch.