The QA layer your app ships without.
Paste a URL or trigger from CI/CD. A real Chrome browser crawls every page on every deploy — catching JS crashes, API failures, mobile breaks, and regressions before users do. Claude AI root-causes each issue and matches it against a growing failure signature library. Scored reliability report in under 2 minutes.
Used by teams shipping with Cursor, Replit, Lovable & Vercel
Failed API Request
Critical
Mobile Overflow
Medium
Who it's for
The QA layer for teams that ship fast
Built for the way modern teams ship — fast, lean, and without a dedicated QA department.
AI Builders
Using Cursor, Replit, Lovable, or Bolt? LLMs write plausible-looking code that breaks silently — broken auth flows, mobile overflows, API crashes at runtime. AgentQA is the real-browser QA pass your AI-generated app never ships with.
SaaS Teams
Ship every sprint knowing regressions are caught before users hit them. Triggered from CI/CD — no test suite to write or maintain.
Founders & Solo Teams
One bug on launch day tanks your momentum. Scan before every deploy — catch the breaking change before it reaches your users.
Dev Agencies
Deliver with a QA report attached. Show clients a health score, not just a Loom walkthrough. Charge for reliability, not just delivery.
Live results
Real reports, running right now
Every card is a live scan that just completed. Click any to see the full report — screenshots, JS errors, network failures, and AI root cause.
Why AgentQA
The QA gap is growing.
AgentQA closes it.
AI coding tools ship apps faster than teams can test them. Traditional QA requires test scripts, dedicated engineers, and weeks of setup. Most AI-built apps skip QA entirely — and bugs reach users instead.
The gap
LLMs generate code that compiles but fails in a real browser
Broken auth flows, mobile viewport overflow, silent API 401s. These only appear when a real browser runs the app — no linter, no type checker, no code review catches them.
AgentQA runs a real Chrome browser on every deploy, detects failures as they happen, and builds a pattern library of your app's real failure history over time.
What it catches
TypeError: Cannot read properties of undefined (reading 'user')
Caught before launch
Content wider than viewport at 375px — horizontal scroll on mobile
Caught in 90 seconds
Static test suites go stale the moment your UI changes. Playwright scripts need an engineer to write them, maintain them, and fix them after every redesign. Manual QA doesn't scale past a certain team size.
AgentQA works on any URL, in any framework, with zero configuration. The reliability intelligence compounds with every scan — not worse.
What gets tested on every deploy
Full-stack coverage, zero configuration
Real browser testing across every discovered page — desktop and mobile — on every deploy.
Issue Detection
404s, JS crashes, broken images, mobile overflow, and failed API calls — classified as critical, medium, or low across every page.
JS Error Tracking
Uncaught exceptions captured with full stack traces and matched against known failure signatures for instant pattern recognition.
Mobile Testing
Every page tested at 375px. Horizontal overflow flagged with side-by-side desktop and mobile screenshots on every scan.
QA Score
A 0–100 reliability score weighted by issue severity. One number to track app health across every deploy over time.
Screenshots & Capture
Desktop and mobile screenshots for every page on every scan. Visual evidence attached to every issue detected.
Network Analysis
Every API call, fetch, and script request — status codes, response times, and failures surfaced in one view.
Recurring Scans
Schedule scans daily, weekly, or after every deploy. Regressions are caught before they accumulate into user complaints.
CI/CD Integration
Trigger from GitHub Actions, Vercel deploy hooks, or any webhook. Reliability intelligence runs on every merge to main.
Not just detection.
Root cause. Fix. Pattern-matched.
Every issue is analyzed by Claude AI. You get the exact technical reason it broke and a targeted fix — ready to paste into your editor.
Every diagnosis cross-references a growing failure intelligence database built from real AgentQA scans. Auth races, null reference chains, mobile overflow signatures — each pattern is matched with higher confidence than a cold model call. That's the moat: real-world software failure data, not just a model.
Error detected
TypeError: Cannot read properties of undefined (reading 'user')
Root cause
The auth context is accessed before the session resolves. useUser() returns undefined on first render — before the session hydrates on the client.
Fix suggestion
// Guard before accessing session
if (!user) return <LoadingSpinner />
// Then safely use user properties
return <Dashboard user={user} />
Add this guard in every auth-protected component before accessing user properties.
Matched against known authentication failure patterns — root cause identified
Cause-level classification
A 401 on an internal API route is diagnosed differently from a 401 on a third-party service — because the fix is different. Classification runs on cause, not error text.
Fix-ready, not reference-ready
Returns the exact guard, wrapper, or null-check to add — scoped to the error type and its location in your code. Not a docs link. Not a StackOverflow thread.
Issue intelligence that compounds
Every scan adds real failure signatures to a shared dataset. Auth races, mobile overflow patterns, silent API failures — each recurrence is matched faster, with higher confidence. A growing QA dataset no static test suite can replicate.
Cross-scan intelligence
Issues get detected.
Then remembered.
Every scan feeds a shared pattern library. Known bug signatures are matched instantly — no Claude call, no latency. Regressions are tracked across deploys, not just per scan.
Pattern memory
Every detected issue is fingerprinted and stored. Repeat occurrences are matched instantly across all future scans.
Signature matching
33 known framework failure signatures — Next.js hydration, Shopify race conditions, Laravel CSRF — matched before AI analysis.
Regression detection
Issues marked resolved are tracked. If a fingerprint reappears in a future scan, it is flagged as a regression — not a new issue.
Compounding accuracy
Each scan refines root cause confidence. High-frequency failure patterns get faster, more accurate diagnosis over time.
No regressions detected — every resolved issue has stayed resolved across all tracked domains.
Live demo
See AgentQA run on a real site
Pick a site below — AgentQA runs the exact same scan your users trigger. Real browser, live results, real findings.
~90 seconds · Real Chrome browser · Publicly accessible sites only
Simple pricing
Start free. Upgrade when you ship.
Free for individual scans. Pro adds CI/CD integration, team seats, API access, and Slack notifications — autonomous QA on every deploy.
Free
Full QA report on every scan — no credit card, no expiry
- Up to 5 pages per scan
- Full page screenshots — desktop & mobile
- Issue classification (critical / medium / low)
- AI root cause analysis + fix suggestions
- Overall health score + per-page breakdown
- Console, JS, and network error detection
- Permanent shareable report link
Pro
Coming soonFor teams running QA on every deploy — join the waitlist to be notified at launch.
- Everything in Free
- Unlimited pages per scan
- CI/CD integration (GitHub Actions / Vercel)
- Slack & email notifications
- API access + webhook triggers
- Team seat sharing
- Priority support
Free forever · No credit card
Your next deploy could break production.
Most regressions are found by users, not the team that shipped them. Automated QA on every deploy changes that.
Free to start · No test suite to write