Semantic Routing
Semantic routing is the practice of classifying user input by its semantic meaning and intent to direct it to the appropriate handler, agent, or response strategy — enabling a single AI interface to manage diverse types of requests intelligently.
理解する Semantic Routing
A general-purpose AI assistant like GAIA receives vastly different types of input: questions about current tasks, requests to send emails, queries about calendar availability, commands to create automations, and general knowledge questions. Each type benefits from a different handling strategy.
Semantic routing sits at the entry point of the system. When a message arrives, the router classifies its intent — is this a tool-use request? A knowledge question? A command to execute a workflow? A clarifying question? — and dispatches it to the appropriate handler with the appropriate context.
Routing can be rule-based (if the message mentions 'email', route to email agent), classifier-based (a model trained to classify intent categories), or LLM-based (asking the LLM itself to determine what type of request this is). LLM-based routing is more flexible but adds latency; classifier-based routing is faster but requires training data.
Good routing dramatically improves response quality and efficiency. Sending a tool-use request to a general-purpose handler wastes tokens on unnecessary context. Routing it directly to the relevant specialist gives it the domain-specific tools and context it needs.
GAIAの活用方法 Semantic Routing
GAIA uses semantic routing to direct incoming messages to the appropriate processing path. Email-related requests go to the email agent with Gmail tool access. Calendar requests go to the calendar agent with Google Calendar integration. General knowledge questions bypass tool-use overhead. This routing layer makes GAIA fast and precise across diverse request types.
関連概念
Agent Loop
An agent loop is the iterative execution cycle of an AI agent in which it reasons about the current state, selects and executes an action (often a tool call), observes the result, and repeats until the task is complete or a stopping condition is reached.
Supervisor Agent
スーパーバイザーエージェントとは、マルチエージェントシステムを統括するAIエージェントです。依頼を受け取ってサブタスクに分け、それぞれを適切なツールを持つ専門のワーカーエージェント(サブエージェント)に任せ、返ってきた結果を確認して一つの回答にまとめます。実行するのはワーカーで、誰が何をいつまでに担当するかを決めるのがスーパーバイザーです。
エージェンティックAI
エージェンティックAIは、自律的に意思決定を行い、複数のステップから成るタスクを最小限の人間の監督で遂行するよう設計された人工知能システムを指します。
Function Calling
Function calling is a feature of AI models that allows them to generate structured, machine-readable invocations of predefined functions, enabling AI systems to reliably call external APIs and tools with the correct arguments.
Prompt Engineering
Prompt engineering is the practice of designing and refining inputs to AI language models to reliably elicit desired outputs, shaping model behavior without modifying the underlying weights.
