Mercari's pj-double: Agent-Spec Driven Development achieves 150%+ development speed improvement
AI usage at Mercari was fragmented — individual developers used different approaches (Vibe Coding) that could not be reproduced or shared organizationally, widening the gap between AI-proficient and non-proficient developers and preventing company-wide productivity gains.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Developer defines task scope
A developer provides objectives, steps, and completion criteria to initiate autonomous agent execution.
Tools used
Claude CodeDX
Outcome
SDD-based projects averaged over 150% development speed improvement versus estimates, and over 80% improvement versus other AI-assisted methods. pj-double expanded from one person to a team of over ten and scaled company-wide across Mercari's full delivery cycle.
What failed first
Early standardization attempts were limited to sharing CLAUDE.md and Cursor Rules locally, without reaching organizational scale. A build trap in QA tooling — delivering a rich UI app instead of minimal prompt infrastructure — raised contribution barriers and slowed method iteration.