Progressive Routing Cuts AI Agent Tool Context Overhead by 95%

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AI agents often underperform because their context windows are overwhelmed by thousands of tokens worth of tool definitions they do not need for a given task. A technique called Progressive Routing addresses this by using semantic search and vector embeddings to match only the most relevant tools to the active prompt. A second layer then injects only the top relevant tool schemas into the model's context, keeping the rest dormant but accessible. This approach reduces tool context overhead from roughly 50,000 tokens down to 1,200–2,500 tokens, a claimed 95% reduction. The method is designed to work uniformly across major AI coding assistants including Claude Code, Gemini CLI, Cursor, and GitHub Copilot CLI through a single shared configuration.

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