30-Minute Daily Series · Free Public Access

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.

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20 minPer Episode
DailyCadence
2-PartConcept + Runbook
FreePublic Access
Concept Video
~15 min deep-dive on the pattern
+
Runbook
~15 min guided execution
=
30 min/day
Real agentic QE capability
Series Progress7 of 7 episodes live

🎬 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

  1. Pulling: Moving requirements into the IDE as version-controlled files (1:30).
  2. Auditing: Using AI to check requirements against INVEST criteria (1:45).
  3. Decomposition: Breaking down monolithic requirements into testable sub-stories (2:00).
  4. Syncing: Reflecting the hierarchy in Azure DevOps (2:15).
  5. Implementation: Leveraging AI to generate code and styles based on the context (2:30).
  6. Verification: Running automated E2E tests to confirm acceptance criteria (2:45).

Benefits: This approach automates manual overhead, allowing product owners, developers, and QA teams to focus on delivering value rather than managing project portals (2:56 - 3:21).

Filter:

Week 1 — Foundation

5 episodes
Day 1
🟢 Live

QE 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

⏱ 20 min🎭 Orchestrator & Observer
OrchestrationGeminiRunbookGit
Day 2
🟢 Live

Git Basics & Sandbox Isolation

Navigating repositories and isolating work branches for secure agent access

⏱ 20 min🎭 Branch Manager & Sandbox Auditor
GitSandboxingWorkspace Control
Day 3
🟢 Live

Requirements Architecture & The INVEST Audit

Auditing stories against the INVEST framework to ensure they are AI-ready

⏱ 20 min🎭 Requirements Architect
INVESTRequirementsQuality Gates
Day 4
🟢 Live

Decomposition & BDD Prompting

Converting failed monolithic requirements into modular BDD stories

⏱ 20 min🎭 Agile Coordinator & Decomposition Analyst
BDDAgile DecompositionUser Stories
Day 5
🟢 Live

Context Engineering & State Persistence

Managing agent state memory using the Daily Handover protocol

⏱ 20 min🎭 Context Engineer
Context ManagementHandover LogsState Persistence

Week 2 — Advanced Patterns

2 episodes
Day 6
🟢 Live

Multi-Agent Delegation & Autonomous Orchestration

Directing specialized AI subordinates to build and test concurrently

⏱ 20 min🎭 Lead Orchestrator & Manager
Multi-AgentDelegationOrchestration
Day 7
🟢 Live

Closed-Loop Defect Tracking & Auto-Repair

Closing the SDLC loop by turning bug discovery into automated repairs

⏱ 20 min🎭 Verification Director
Auto-RepairDefect TrackingClosed-Loop Testing