A Full AI-Driven Software Workflow, from PRD to Review, Without Writing a Line of Code
This workflow shifts AI coding from one-shot prompts to a repeatable, team-like process. The key insight — that project documentation, not chat history, is the real long-term memory — means any new AI session can pick up exactly where the last one left off, cutting token waste and inconsistent decisions.
Starting from an open API, a developer ran a complete software R&D workflow using only AI, never writing code. The AI first analyzed the API and produced a PRD, roadmap, and architecture docs. When Figma's MCP hit a paywall, the AI built its own JSON-driven wireframe viewer to keep the prototype phase free and machine-readable. A design system was then translated into Compose runtime mappings — Theme, Component, and Page mappings — so the AI could generate UI code directly from specs. Before any feature work, an engineering bootstrap laid down shared infrastructure: dependency injection, navigation, theme, error handling, and platform contracts. A "Ponytail" skill was installed to enforce minimal implementation, preventing the AI from over-engineering. The project's AGENTS.md was refactored into a feature development guide that freezes design assets, defines a source of truth, and sets a Definition of Done. With that foundation, a single prompt — "Develop the homepage" — triggered a full plan, impact analysis, coding, build verification, and review cycle. The result is a runnable Kotlin Multiplatform app and a reusable workflow where the prompt shrinks as the project's memory grows.
Prompt length shrinks as project knowledge grows. The endpoint of prompt engineering is project engineering — embedding rules, specs, and memory into the repo so the AI needs only a task name.
AI over-engineers by default. A skill like Ponytail that forces a checklist — can I reuse, can the platform do it, does a dependency already cover it — is essential to keep output minimal and maintainable.
Figma and similar tools are overkill for AI-driven prototyping. A JSON-driven wireframe viewer gives the AI a stable intermediate language that feeds directly into code generation.
Design tokens alone are not enough for AI coding. A runtime mapping layer (Theme Mapping, Component Mapping, Page Mapping) is needed to translate design language into framework-specific code.
Multi-agent architectures are the logical next step: separate agents for product, UX, visual, architecture, and engineering, each with their own memory, boundaries, and Definition of Done, collaborating through documents and rules.