— Use case · Automated testing

Test flows on your web app.

Sign-up, login, checkout, onboarding — A3 learns your critical user journeys once and replays them deterministically as end-to-end tests. Assert on outcomes, catch regressions, and auto-fix flows when the UI changes.

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Sign-up Auth Checkout Other
"Lady Justice" — every flow, weighed.
— The testing gap

Most E2E suites break when the UI moves.

Teams need confidence that signup, checkout, and core journeys still work — but traditional tests bind to selectors, flake in CI, and require a rewrite every redesign. Maintenance eats the time that should go into shipping features.

Why E2E tests fail to scale

Record-and-playback and hand-written selectors assume the DOM stays still. One component library upgrade or A/B test can turn a green suite red overnight.

How A3 closes the gap

A3 learns each journey as typed stages with assertions baked in. Stages match page state, not fragile CSS paths — and the learning agent auto-fixes them when the UI shifts.

Traditional E2E

Selector brittleness — tests fail when a button moves, a label changes, or a modal adds a wrapper div.

With A3

Stage matching — each step declares what page state it expects; the runner picks the stage that fits, not a single hard-coded path.

Traditional E2E

Flaky replays — timing races and non-deterministic waits make CI red without a real regression.

With A3

Deterministic runner — compiled Playwright stages replay with no LLM in the loop; same inputs, same path, every run.

Traditional E2E

Redesign = rewrite — a UI refresh means days re-recording flows or chasing broken locators across dozens of files.

With A3

Self-healing stages — when a step stops matching, the learning agent auto-fixes the stage in real time.

Traditional E2E

Opaque failures — "element not found" with no context about which step or what the page looked like.

With A3

Traces per stage — every run records Playwright traces; a failed assertion points at the exact stage and line.

Traditional E2E

Tests without assertions — scripts click through but never verify the user landed in the right state.

With A3

Outcome checks — stages assert on URLs, content, and captured outputs; failures surface with a clear message.

— Common concerns

E2E testing should keep up with shipping, not slow it down.

Teams worry that browser tests are slow, flaky, and expensive to maintain. A3 treats journeys like production automation: learn once, replay deterministically, self-heal on drift, and fail with traces you can actually debug.

— Reliability

Self-healing when the UI changes

A moved button should not mean rewriting the suite. When a stage stops matching, the learning agent auto-fixes it in real time — so CI keeps running while the UI evolves.

— Speed

Fast enough for every commit

Learning uses an agent once; CI does not. The runner replays typed stages with no LLM in the loop — fast, repeatable runs that fit your pipeline without model spend on every push.

— Confidence

Assertions on every outcome

A journey that clicks through without checking the result is not a test. Each stage can assert on URLs, visible content, and captured outputs — so a regression fails with the stage, message, and trace, not a vague timeout.

— Operability

Debuggable failures in CI

Stages and playbooks live in git; trigger runs on push or a schedule — locally or in the cloud. Playwright traces, structured logs, and per-stage pass/fail make red builds actionable in minutes.

— Across journeys

The same model for every critical flow.

Sign-up, auth, checkout, and form validation look different in the UI — but the testing model is the same: describe the journey, learn each step as a stage, assert on the outcome, replay on every run.

— Sign-up

Onboarding from first visit to active user

Registration wizards, email verification, and profile setup are multi-step flows that break easily — and are exactly what you cannot afford to ship broken.

  • Why test — onboarding is the first impression; a broken step silently kills conversion.
  • Multi-step wizards — each screen is a stage; the runner verifies the user advances and lands logged in.
  • Email and verify steps — inject test fixtures or stub verification codes at runtime.
  • State assertions — confirm account flags, default settings, and landing routes after completion.
— Auth

Login, SSO, and permissions

Auth flows gate everything else — login forms, password reset, SSO redirects, and role-gated routes need continuous verification as identity providers and UI change.

  • Why test — a broken login blocks every other journey; role bugs expose the wrong data to the wrong user.
  • SSO and redirects — stages follow the real redirect chain, not a mocked shortcut.
  • Password reset — exercise forgot-password and token flows against staging.
  • Role-gated routes — assert that admin, member, and guest see the correct screens.
— Checkout

Cart to confirmation

Purchase journeys touch cart, shipping, payment, and confirmation — the highest-value path in most apps and the one most sensitive to pricing and payment regressions.

  • Why test — revenue depends on checkout working; silent failures here are the most expensive.
  • Test cards and fixtures — inject staging payment details and seeded products at runtime.
  • Promo and shipping — verify discount codes, delivery options, and tax lines update the total.
  • Confirmation asserts — order id, status, and receipt content checked on the final stage.
— Other

Forms, settings, and edge cases

Search, filters, settings panels, and validation errors — any flow where bad input or a layout change should surface the right message, not a blank screen.

  • Why test — edge cases regress quietly; users hit them before your team does.
  • Valid and invalid input — submit both and assert on inline errors and field highlight states.
  • Settings and preferences — toggle options and verify persistence after reload.
  • Same answer — learn the flow once; replay in CI with assertions baked into every stage.
— Test your app

Describe the journey. Get a suite that keeps up.

Describe the journey in the Product ADE, learn your flows once, then wire the runner into CI — deterministic, self-healing, and debuggable when something breaks.

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