LLM Routers: Architecture, Governance, and Security in AI Systems

This article delves into the architecture and governance of LLM routers, which distribute requests across various AI models based on their capabilities, costs, and response times. It argues that in agentic and multi-agent systems, routing is a critical decision boundary for authority, data, resources, and policy continuity. The analysis distinguishes between routers, gateways, and control planes, comparing different selection methods and examining benchmarks for quality, cost, latency, and robustness. The author proposes a framework, SGAEIA, for assessing governed routing, emphasizing that savings per call do not always equate to savings per task and that availability does not guarantee admissible substitution. AI

IMPACT Provides a framework for understanding and implementing governed LLM routing, crucial for optimizing costs and ensuring security in complex AI systems.

RANK_REASON The item is a technical review and analysis of LLM routing, presenting a framework and discussing related concepts, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

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• Aridio Silva
• Brazil
• Creative Commons Attribution 4.0 International
• ORCID iD
• SGAEIA

• infra
• other

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