smart-router

AI agent task routing optimizes LLM costs by classifying incoming requests and delegating simple tasks to cheaper models like Haiku, while reserving powerful models like Opus for complex code or research tasks.

You can set up intelligent task delegation by configuring specific AI agents and using a routing system to evaluate message complexity, automatically spawning the appropriate predefined agent for execution and feedback processing.

Yes, AI agents can learn from past interactions by maintaining institutional memory through logs and learned rules, updating files like lessons-learned.md and rules.json to improve future routing accuracy.

The best way to categorize AI requests is using an intelligent classification system that analyzes incoming messages to sort them into categories like simple, lookup, code-complex, research, reasoning, and creative.

Yes, you need specific agent configurations established before implementation, as the router requires predefined templates to seamlessly spawn other agents like a dev agent for complex code debugging.

Task delegation to cheaper models fails for complex requests when the intelligent router incorrectly classifies the message complexity, highlighting the need for institutional memory to refine routing rules and prevent mismatched model assignment.