LLM Routers for AI Systems: Architecture, Governance, Multi-Agent Systems, Swarms, and Security

Language model routing distributes requests across models, providers, and configurations with different capabilities, costs, and response times. Its usefulness, however, depends on two distinct decisions: establishing which alternatives are admissible for the task and selecting the most suitable configuration from that set. This article presents a technical review and argues that, in agentic and multi-agent systems, routing forms part of a decision boundary concerning authority, data, resources, and policy continuity. The analysis distinguishes routers, gateways, and control planes; compares rules, classifiers, similarity, cascades, and model-driven selection; and examines what benchmarks establish about quality, cost, latency, and robustness. Its analytical contribution is an SGAEIA-related framework for assessing governed routing, applied to model substitution, budgets, and multi-agent execution. The analysis explains why savings per call do not equal savings per task, availability does not establish admissible substitution, and average accuracy does not demonstrate trajectory integrity. Adoption should depend on observed gains against simple baselines, accompanied by sufficient evidence about the boundaries and consequences of decisions.