The words around AI routing, defined so they can be quoted.

An LLM router is a component that sits between an application and several large language models and decides, for each request, which model should answer it - by reading the request and scoring the candidates on quality for that task, cost, latency and the caller's rules.

AI routing is the practice of deciding, per request, which AI capability should handle it - a model, an agent, a tool, a skill, a workflow or a person - under the caller's policy, with a record of why.

An AI gateway is a single API endpoint placed in front of several AI providers that handles keys, rate limits, retries, logging and - in a routing gateway - the decision about which model answers each request.

Model selection is choosing which AI model should handle a given request. Automatic model selection makes that choice per request from the request's own signals and the candidates' measured quality, cost and latency, instead of once in configuration.

Agent routing is deciding, per request, whether an AI agent - rather than a plain model, a tool or a person - should handle it, and which agent; it ranks agents next to the other targets under the same policy and records the choice.

MCP, the Model Context Protocol, is an open protocol through which AI assistants and agents discover and call tools, read resources and use prompts exposed by a server; an MCP router is an MCP server whose tools make the routing decision, and a router that imports other MCP servers as routing targets.

LLM cost is what an application pays to call language models, almost always priced per million input and output tokens with rates that differ by one to two orders of magnitude between models; LLM cost optimisation lowers it by sending each request to the cheapest model that meets the quality required.

AI governance is the set of controls that decide where AI requests may go, what they may contain, what they may cost and who may send them - and the records that prove the controls were applied. Enforced in a routing layer, it is a control on the path of every request rather than a document beside it.

Model fallback is what a router does when the chosen model fails, times out or is saturated: it re-routes the request to the next best candidate under the same rules instead of returning the error, and records both the failure and the recovery.