What I shipped

• End-to-end AI SDLC pipeline: spec → plan → implementation → review → audit
• HITL gates between phases, each backed by a specialized agent role
• Custom slash commands and agent definitions tested against my own production work
• Multi-IDE coverage (Claude Code, Cursor, Windsurf, Codex, GitHub Copilot)

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: custom slash commands orchestrating 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
• Built the system using the system itself — proving AI can build its own tooling
• Full automated flow: one command triggers spec → implement → push → review → audit → complete

Key Insight