Build a test-automation framework where the work is done by AI agents, not scripts you run by hand. Most automation courses teach you to write scripts. This one teaches you to build a team — a roster of purpose-built Claude Code subagents that plan, write, review, run, and debug tests by reasoning about the work, using their own tools, and handing off to each other in a real engineering loop.
You’ll author each specialist from scratch and wire it into a working pipeline:
- plan-architect researches a task and returns a reviewable plan before a line of code is written
- automation-writer turns user stories and acceptance criteria into non-flaky Pytest + Playwright tests
- senior-code-reviewer reviews the diff read-only and opens the PR on approval
- test-suite-planner decomposes a story into prioritised, independent suites as Jira sub-tasks
- test-runner executes each reviewed suite and posts a sanitized verdict
- qa-failure-investigator tells a test bug from a product bug — by attempting the fix — and files evidence-backed defects
A lean orchestrator routes work between them and carries context across the hand-offs. Because the heavy lifting stays inside each agent’s isolated context window, the orchestrator runs a long, multi-stage workflow without exhausting its own — in one measured build it held at ~5%
