I Cut 80%+ of Context Overhead in My Coding Agent
A developer reports that AI coding agents often waste significant context window space by loading dozens of unused tool definitions by default. By optimizing these agent harnesses, the author was able to reduce context overhead by over 80%, improving efficiency and model performance.
Why it matters
Optimizing context usage is critical for developers building AI agents, as it directly impacts latency, token costs, and the reasoning quality of large language models.
When you start a session in a modern AI coding agent, a huge chunk of your context window is consumed before you type your first message.
Between system instructions, formatting rules, MCP server integrations, and dozens of registered tool schemas, most agent harnesses dump 10,000 to 25,000+ tokens of static overhead into the context window on every turn.
On 90% of turns, an agent only needs basic file and shell tools ( read , bash , edit , write ). Specialized tools like browser automation, image generation, web search, or background task runners are needed occasionally, sometimes only once a week.
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.
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