saga-dual-loop-router

Dynamic AI task routing optimizes LLM selection by evaluating task complexity, entropy, and system resources to assign requests to the most appropriate model. It balances performance, cost, and accuracy by matching task requirements with model capabilities.

A dual-loop cognitive architecture for model optimization uses an inner and outer loop to manage AI orchestration. It dynamically evaluates task complexity and system load to route requests between fast and slow language models, ensuring efficient resource utilization.

You route complex strategic planning to the right language model by using a routing engine that estimates task complexity and entropy. High-complexity requests are directed to powerful models, while simpler tasks are routed to faster, more efficient models.

Yes, you can downgrade AI tasks to manage model quotas under heavy load. The routing engine automatically shifts less critical tasks to more efficient models, preserving expensive resources for high-priority strategic planning and execution.

You should use fast thinking models when tasks are simpler or the system is under heavy load. Slow thinking models are reserved for complex, high-entropy tasks like strategic planning where maximum accuracy and processing power are required.