AI-Native SDLC Workflow
120+
commits in
~/.claude repo
~/.claude repo
21
slash commands
for full SDLC
for full SDLC
8
specialized
agent roles
agent roles
Full
SDLC coverage
AI-augmented
AI-augmented
Challenge
Traditional PM tools don’t leverage AI — planning, review, and QA remain manual bottlenecks. Even with AI-assisted coding, the surrounding workflow (task specification, code review, quality audits, PR management) stays human-driven and slow.
Approach
Built a complete AI-native PM system from scratch: 21 slash commands orchestrating 8 specialized agent roles across every SDLC phase.
- Expert Review Panels — 4 expert perspectives (Architect, Completeness Analyst, Risk Assessor, Testability Reviewer) analyze every plan before any code is written
- Multi-agent code review — 5 parallel reviewers examine code from different quality dimensions
- Implementation Lock — only 1 command can write code, ensuring all changes go through the structured pipeline
- Resumable state machine — full task state persists in GitHub Issues, not AI memory, enabling cross-session continuity
Results
- Every SDLC phase AI-augmented: spec → implement → review → audit → complete
- Consistent quality without manual review bottleneck
- Resumable workflows — task state persists in GitHub, not AI context
- 120+ commits building the system itself, proving AI can build its own tooling
- Full automated flow: one command triggers spec → implement → push → review → audit → complete
Key Insight
“AI works best with structured workflows and constraints — not open-ended chat. Give AI a clear process, strict boundaries, and persistent state, and it becomes a reliable engineering partner.”
Interested in AI-powered workflows?
Let’s discuss how structured AI automation can transform your development process.