QE Agentic
Workflow Roadmap
To stay relevant in the age of AI, Quality Engineers must transition from manual execution to orchestration. Learn to act as an AI orchestrator and observer of AI outcomes—directing teams of specialised agents for day-to-day technical tasks. Every episode pairs a concept video with a hands-on runbook.
🎬 Series Overview & Overall Goal
This video introduces the Agentic Markdown SDLC (Software Development Life Cycle), a method designed to bridge the gap between high-level project management portals like Azure DevOps and the actual codebase (0:00 - 0:25).
Key Concepts
- Local Source of Truth: By treating the product backlog as a version-controlled codebase where epics, features, and user stories are stored as Markdown files, teams can enable native AI accessibility and automated requirement refinement (0:27 - 0:50).
- Dual-Purpose Files: Each requirement file contains YML front matter for routing and Markdown bodies with Gherkin scenarios to create executable specifications (0:52 - 1:03).
- Bidirectional Sync: A custom CLI tool synchronizes task statuses and hierarchies between local Markdown files and Azure DevOps, ensuring the portal remains updated without manual effort (1:05 - 1:16).
The End-to-End Cycle
- Pulling: Moving requirements into the IDE as version-controlled files (1:30).
- Auditing: Using AI to check requirements against INVEST criteria (1:45).
- Decomposition: Breaking down monolithic requirements into testable sub-stories (2:00).
- Syncing: Reflecting the hierarchy in Azure DevOps (2:15).
- Implementation: Leveraging AI to generate code and styles based on the context (2:30).
- Verification: Running automated E2E tests to confirm acceptance criteria (2:45).
Week 1 — Foundation (Days 1–7)
5 episodesQE as AI Orchestrator — Lab Playbook 1
Direct Gemini 3.5 Flash through a 4-phase local workspace setup, git acquisition, markdown task definition, and automated execution loop
Git Basics & Sandbox Isolation
Navigating repositories and isolating work branches for secure agent access
Requirements Architecture & The INVEST Audit
Auditing stories against the INVEST framework to ensure they are AI-ready
Decomposition & BDD Prompting
Converting failed monolithic requirements into modular BDD stories
Context Engineering & State Persistence
Managing agent state memory using the Daily Handover protocol
Week 2 — Enterprise CI/CD & Governance (Days 8–14)
9 episodesMulti-Agent Delegation & Autonomous Orchestration
Directing specialized AI subordinates to build and test concurrently
Closed-Loop Defect Tracking & Auto-Repair
Closing the SDLC loop by turning bug discovery into automated repairs
Headless Agent CI/CD Runners & Secret Sandboxing
Containerize agent CLI execution within GitHub Actions and Azure DevOps pipelines with zero-leak secret protection
Automated PR Review & BDD Criteria Auditing
Configure agents to audit incoming PR diffs against Given-When-Then BDD specifications and enforce token spend caps
Policy-as-Code & Quality Gate Enforcers
Enforce token spend caps ($0.50 per pipeline run), linting compliance, and security static analysis before permitting agent auto-commits
Closed-Loop Self-Healing CI Test Runners
Intercept failing Playwright and unit test steps in CI, parse stack traces, and allow agents to open targeted auto-fix pull requests
Human-in-the-Loop Approval & Webhook Routing
Route high-impact agent PRs to Slack/Teams channels with explicit interactive reviewer approval hooks before merge execution
Enterprise Audit Ledgers & Compliance Tracking
Maintain immutable JSON audit logs tracking agent decision trajectories, prompt hashes, and SOC2/ISO compliance markers
Enterprise Autonomous QE Pipeline Capstone
Construct an end-to-end production-grade enterprise CI/CD pipeline combining PR auditing, self-healing, token caps, and approval gates