Agentic AI SDLC
A Comprehensive Guide to Spec-Driven, Agent-Native Software Engineering
The lifecycle in this book is a learning loop:
Discover → Specify → Design → Plan → Execute → Verify
↑ ↓
└──── Learn ← Operate ← Release ← Review ───┘
At every stage, answer five questions:
- What outcome and constraints define success?
- What authority does the agent have?
- What artifact or change must the stage produce?
- What evidence permits progression?
- Where does failure return the lifecycle?
Learning Paths
| Reader | Recommended path |
|---|---|
| Individual engineer | Parts I, III, VI, VII, X, XI |
| Technical lead | Parts I, V, VI, X, XI, XIV |
| Platform engineer | Parts VII–XIII |
| Security/governance engineer | Parts IV, V, IX, XII, XIV |
| Brownfield modernization team | Parts II, III, VI, X, XIII, XIV |
How to Use the Book
Each part is a compact reference containing several numbered chapters. Tutorials build on TaskFlow checkpoints; framework labs start from the same baseline and use identical acceptance criteria. Do not copy a policy or command merely because it appears in an example—verify the snapshot date and adapt authority to your environment.
The book is vendor-neutral by design. Frameworks are compared by artifact model, change model, human gates, validation, context preservation, parallel execution, portability, governance, and maintenance burden. No framework is declared universally best.
Begin with From Code Assistance to Agentic Execution.
Keep the TaskFlow Reference Artifacts open while working through the chapters. It contains the canonical outcome, requirements, state invariants, OpenSpec delta, task contract, permission policy, evaluation package, release evidence, and incident-to-correction trace used throughout the guide.