Agentic Routing

Overview

A Router is a specialized agent architecture that enables an LLM to select a single step from a predefined set of options. It offers a constrained yet effective way to control decision-making, ensuring precision and reliability in specific applications.

Routers are ideal for scenarios where an LLM must make a single decision or route a task based on context, without the complexity of managing an entire control flow.

Agentic Routing

Key Features of Routers

  1. Decision-Making Scope:

    • The LLM is presented with a predefined set of options (e.g., tools, APIs, or pathways).

    • It selects the most appropriate option based on the input context.

  2. Deterministic Output:

    • The LLM’s decision is limited to a constrained set of outcomes, enhancing predictability.

  3. Focused Control:

    • Routers are designed to handle specific, single-choice decisions, reducing complexity while maintaining flexibility.

How Routers Work

  1. Input: The router receives a user query or task.

  2. Evaluation: The LLM evaluates the query against predefined options.

  3. Routing: It selects the appropriate step or tool to proceed.

  4. Execution: The selected tool or path is executed to complete the task.

Examples of Router Applications

  • Tool Selection: An LLM determines which tool to call for specific tasks, such as querying a database or sending an email.

  • Pathway Routing: Routing queries to different models or workflows based on their complexity or type.

  • Validation: Deciding if additional steps are needed or if a generated response is sufficient.

Benefits of Routers

  • Efficiency: Simplifies decision-making with predefined choices.

  • Reliability: Constrained options reduce the likelihood of errors.

  • Flexibility: Can adapt to various tasks within the defined scope.

Routers exemplify a minimal yet powerful approach to enabling control flow in LLM-based systems, making them a cornerstone of modern AI agent architectures.