How I Cut 80%+ of Context Overhead in My Coding Agent
A developer reveals how he slashed context overhead in his AI coding agent by over 80%, addressing a problem where most harnesses dump 10,000–25,000+ tokens of static tool schemas into the context window on every turn. He benchmarks popular tools like Codex, Antigravity, and Claude Code, showing that even with fewer tools, schema bloat persists. His solution combines two principles: consolidating tools by action instead of CRUD, and dynamic tool activation that keeps only four core tools active, with others on standby and activated on demand via bash interception, cutting turn-zero tokens dramatically.
Leaving 25 to 80+ tool definitions active in the LLM function schema 100% of the time wastes tokens, increases latency, and degrades model reasoning by polluting the attention space with irrelevant parameters.