From Manual Test Design to Agentic Quality Engineering

Most QE teams use AI the way you’d use a calculator — one task at a time, when you remember to reach for it. This module shows you how to build something better: a coordinated system of QE Agents working across your entire delivery lifecycle.You’ll design nine purpose-built agents — from requirements intelligence through release decisions — each with defined inputs, outputs, confidence thresholds, and human review gates. Because an agent that runs without accountability isn’t a system. It’s a liability.Whether you’re an embedded SDET running agents at the merge request or part of a separate QE team operating them as pipeline gates, the architecture is the same. Only the trigger point changes.

Build it yourself

In a 15-minute hands-on exercise, you’ll build and run a working MR Delta Coverage Agent — one that generates targeted test coverage for exactly what changed in a merge request, including the dependency risks most reviewers miss. You’ll run it against a real MR from your own sprint.

What you’ll walk away with

A deployable QE Agent workflow blueprint built from your own practice — named agents, generation points, confidence thresholds, human checkpoints, and quality gates. Not a template. Something you can present to your team tomorrow.

Who this is for

QE professionals, SDETs, and QA engineers who want AI working systematically across their lifecycle rather than task by task. Built for both embedded SDET teams and separate QE teams.