State-Dependent Routing

Gold definitionUpdated Apr 2, 2026

Definition

State-Dependent Routing is a mechanism within multi-agent systems that dynamically selects appropriate agent roles and model scales based on the current reasoning stage and task complexity. It optimizes computational efficiency by avoiding uniform deployment of large models, leading to significant cost reduction and improved accuracy.

At a glance

Executive summary

State-Dependent Routing is a smart way for AI systems to choose the right size of AI model for each part of a task, instead of always using the biggest one. This makes the system much faster and cheaper to run, while also making it more accurate for complex problems.

TL;DR

It's a system that intelligently picks the best-sized AI model for each step of a task to save money and improve results.

Key points

Dynamically selects agent roles and LLM scales based on reasoning stage and task complexity
Addresses computational inefficiencies and uniform deployment of large LLMs in multi-agent systems
Used by researchers and engineers developing efficient multi-agent systems, adaptive AI, and LLM-based reasoning
Contrasts with existing frameworks that deploy large LLMs uniformly across all agent roles, regardless of cognitive demands
Focus on adaptive resource management, cost-efficiency, and performance optimization in complex multi-agent LLM systems

Use cases

Complex Scientific Discovery: Dynamically allocating different LLM scales for hypothesis generation, experimental design, and result analysis in a multi-agent scientific research system.
Autonomous Decision-Making: Routing different stages of decision processes (e.g., perception, planning, execution) in robotics or self-driving cars to appropriately sized models to ensure real-time efficiency.
Customer Service Chatbots: Using smaller, faster models for common queries and routing complex, nuanced questions to larger, more capable LLMs, optimizing response time and cost.
Financial Market Analysis: Employing smaller models for routine data parsing and trend identification, while engaging larger, more sophisticated models for deep dive analysis of anomalous market events.

Also known as

Adaptive Model Selection, Dynamic Model Routing, Context-Aware Routing, Cognitive Demand Routing