What Building PPI.Studio Taught Me About Governing AI Coding Agents

Description and use

Building PPI.Studio revealed that AI coders generate code fast but often compress or misrepresent details. Implementing a strict governance system where humans own production code and AI only suggests changes with full replacement paths ensures reliability and transparency.

Problem

AI coding agents can produce code quickly but often compress or misrepresent technical details, leading to incorrect architectural decisions.

Outcome

The governance framework prevented semantic compression errors, ensured complete replacement paths, and maintained human accountability, turning AI agents into reliable engineering accelerators.

Workflow

• 1. Define a clear human‑owned production code base under src/main/.
• 2. Use src/test/ as the agent‑maintained proving ground.
• 3. Treat interfaces and contracts as change‑control boundaries with API impact analysis.
• 4. Enforce constructors to perform dependency assignment only.
• 5. Require every important claim to have an evidence class (Implemented, Tested, etc.).
• 6. When an agent suggests a change, provide the full replacement path and affected classes.
• 7. Conduct regular audits to verify build success and identify incomplete modules.
• 8. Maintain a closed‑loop workflow: evidence normalization → conflict detection → scoped analysis → recommendation → scientist review → assay feedback → updated evidence.

Evidence

The failure was not a fabricated SDK boundary. It was semantic compression.

Source

The responsible answer must identify the exact class, method, registration mechanism, and execution path that makes it happen.

Source