Agent Routing
The process of directing incoming requests to the most appropriate agent based on task type, complexity, user context, or current system load. Agent routing acts as the traffic controller for multi-agent systems.
Agent routing determines which agent handles which request in systems with multiple specialized agents. A router analyzes the incoming request, classifies its type and complexity, considers the available agents' capabilities and current load, and directs the request to the best match. This can be implemented as a rules-based system, a classifier model, or another agent.
For customer-facing products with diverse use cases, routing quality directly impacts user experience and cost efficiency. A well-designed router sends simple FAQ-style questions to a lightweight retrieval agent (cheap and fast) while routing complex troubleshooting requests to a full-featured reasoning agent (expensive but thorough). Routing can also incorporate user tier (premium users get more capable agents), language, geography, and domain specialization. Implement routing as its own measurable component with metrics on routing accuracy, latency overhead, and downstream agent success rates. Common patterns include intent classification routing, keyword-based routing, and LLM-based routing where a small model analyzes the request before handing it to a larger agent.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.