Using GitHub Copilot as an autonomous coding agent revealed that most inefficiencies stem from poor documentation and organizational practices, not code quality. Key findings: documentation sprawl caused the agent to spend ~40% of its time determining which docs to trust; running full production builds for simple validation tasks wasted 30-60 seconds per check; and ambiguous edge case handling led to 15+ minutes of circular reasoning. Fixes included consolidating docs into a single source of truth, writing focused validation scripts, and explicitly documenting edge cases. Results showed ~40% reduction in processing time, ~75% reduction in token usage, and >80% reduction in confusion. The core insight is that optimizing for AI agents is really just good engineering — removing ambiguity and making implicit knowledge explicit benefits humans and bots alike.
