Engineering QA Engineer

An AI tester that catches bugs before users do

Tests the product after every deploy, logs bugs with full context, and tracks what's been fixed. Your first line of defense against regressions.

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Capabilities

What your QA Engineer worker does

01

Runs predefined test scenarios after each deploy automatically

02

Logs bugs with steps to reproduce, screenshots, and severity rating

03

Tracks bug resolution status and flags overdue fixes

04

Maintains a testing checklist per feature area

05

Retests fixed bugs to confirm resolution, regression checks

06

Reports weekly on product stability and bug trends

Goal example

"Run all test scenarios within 1 hour of deploy. Catch 90%+ of bugs before users report them."

That's the entire setup. No prompts. No workflows.

Differentiators

What makes this different

Instant post-deploy testing

Every deploy triggers a full test run, automatically. Bugs are caught and logged before your first user hits the new code.

Context-rich bug reports

Not "it's broken." Every bug comes with steps to reproduce, expected vs. actual behavior, screenshots, and severity, everything an engineer needs to fix it fast.

Regression guardian

Fixed bugs don't come back. Your AI tester retests every resolved bug after each deploy to make sure the fix holds.

Stability tracking

Weekly reports show bug volume trends, resolution speed, and which feature areas are most fragile. Your team sees the big picture, not just individual tickets.

Same team. Different output.

Before

  • • Users reporting bugs before the team knows about them
  • • Bug reports missing steps to reproduce
  • • No consistent testing after deploys
  • • Fixed bugs reappearing in later releases
  • • No visibility into product stability trends

After

  • Bugs caught within an hour of deploy
  • Every bug logged with full context and reproduction steps
  • Automated test runs after every deploy
  • Regression testing confirms fixes hold
  • Weekly stability reports with trend data

FAQ

Common questions

Your AI QA Tester runs test scenarios after every deploy, logs bugs with full context (steps to reproduce, screenshots, severity), tracks bug resolution, and performs regression testing, all autonomously.

It handles the repetitive testing work, running checklists, logging bugs, regression checks. Your QA engineers focus on edge cases, exploratory testing, and test strategy.

Your AI tester works on staging environments. It runs your predefined test scenarios and can be configured for different browsers, devices, and user flows.

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From the blog

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Insights

How Engineering Teams Use AI Workers to Ship 2x Faster

Engineering teams are using AI workers to eliminate bottlenecks in QA, code review, documentation, and incident triage. Here's how they ship 2x faster.

Feb 14, 2026

They gave you a tool. We'll give you a team.

Your first Shadow Worker is ready in 30 seconds. No contracts, no workflows to build, no AI to babysit.

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