Structured docs boost coding agent precision

Hot Take: Treating raw source code as the sole interface for AI coding agents is a massive waste of resources; without a structured documentation layer, companies are paying agents to relearn their codebases from scratch on every run.

• –**Substantial Cost Reductions:** Cutting per-task token consumption by 50% translates directly to massive savings (e.g., $500k back on a $1M annual token spend).
• –**Encoding Intent Over Implementation:** While raw code details *how* a system was built, structured docs provide the missing "intent layer" explaining *why* architectural decisions were made.
• –**Model Scalability:** More capable reasoning models do not make documentation obsolete; instead, structured search scales with model capability, ensuring better overall results.
• –**MCP as the Standards Bridge:** Utilizing MCP to search structured docs enables single-query context retrieval, avoiding expensive and unreliable file crawling.