Optimizing Agent Efficiency by Dynamic Context and Model Routing

Developers are using quick evaluation models to dynamically select the exact skills or compute effort needed for AI agent tasks. One implementation adjusts reasoning levels mid-task inside coding environments, while another selects and injects only relevant tool instructions into Claude Code contexts to slash token usage by 88%. This strategy allows AI harnesses to run complex workflows with substantially lower latency and expenses.