Best practices for building an AI agent router including routing strategies, model selection, and evaluation patterns to send each request to the right agent.
An AI agent router serves as the decision-making layer that manages how user requests are routed to the correct function, service, or action within a system. This component is particularly important in large-scale conversational systems where multiple intents, services, and actions are involved. AI agent routing helps route requests that ensure efficiency, scalability, and accuracy.
As an example, if a user was interacting with a system and asked: “What is the weather like in New York City today?” The agent router will determine that this is a weather-related request and it will route the request to a weather-related API to fetch the current weather for New York City.
Whether you’re working on conversational AI, NLP applications, or integrating multiple services, this overview will help you streamline your routing logic and optimize performance. In this short blog and corresponding video below, we cover some best practices for building an agent router including when to implement one and implementation approaches, how to choose one.
Watch: AI Agent Routing Best Practices
When to Implement an Agent Router
Routers are not used by all agents. Some frameworks like LangGraph or OpenAI Swarm spread the job of routing across nodes within an agent instead. Here are some scenarios where agent routers are particularly valuable:
- Systems with multiple service integrations, including various APIs, databases, or microservices
- Applications handling diverse types of user input, especially in NLP-based systems
- Architectures requiring modular, scalable design patterns
- Systems needing sophisticated error handling and fallback mechanisms
Generally speaking, more complex and/or non-deterministic agents benefit from routers.
Implementation Approaches
Routers typically use one of three different techniques to handle their core routing function: function calling, intent-based routing, and pure code routing. In this section, we’ll briefly touch on each of these techniques and why you might use them.
It’s also important to note that when it comes to selecting an agent routing approach, your choice should be guided by several key factors:
- System complexity requirements
- Scalability needs
- Performance constraints
- Maintenance considerations
Whether you’re implementing function calling with LLMs, intent routing, or pure code solutions, ensure your routing logic maintains modularity, scalability, and ease of maintenance. Consider starting with a simpler approach and evolving the system based on actual usage patterns and performance metrics.
Function Calling with LLMs
This approach uses an existing LLM to chose between a set of available functions, each representing a skill or branch in the agent. Most modern LLMs are now equipped with function calling capabilities, and as a result you’ll often see agents using GPT-4o, Claude 3.5 Sonnet, or Llama models as routers.
One of the advantages of this approach is that there is dynamic and flexible processing of complex user inputs. There are minimal routing logic requirements.
But there are also some challenges with function calling:
- Higher latency due to real-time LLM processing
- Resource-intensive operations
- Limited control over granular routing logic
- Complexity in implementing fallback strategies
Function calling routers are the most flexible routing option, however they are also the hardest to control. Introducing a function calling router means introducing another stochastic LLM call that you need to manage. Depending on your agent, the extra flexibility this method adds may be worth it, but if your agent’s routing can be handled using one of the methods below, you’ll reduce your overall testing burden.
Intent-Based AI Agent Routing
Intent routers identify user intentions from queries and map them to predefined functions or services. This approach is prevalent in chatbots and virtual assistants where user queries must be categorized into distinct intents.
This approach works well when your agent has a somewhat limited set of capabilities, each which can be mapped to a distinct intent. This technique can struggle with more nuanced intents however.
Advantages of intent-based routing:
- Clear structural separation between user input and backend processes
- Straightforward debugging and scaling capabilities
- Easy extension of routing logic for new intents
But there is also limited flexibility with ambiguous queries, and requests outside predefined categories can present some difficulty.
Pure Code Routing
For simpler systems, implementing routing logic directly in the application code without external AI or NLP models can be effective. This approach involves hardcoded routing decisions based on predetermined patterns or rules.
This approach is obviously more limited by the needs of your agent, however if you are working with an agent who’s routing can be hard-coded in this way, pure code routing is recommended.
Advantages of pure code routing:
- Superior performance and efficiency
- Complete control over routing logic
- Optimization capabilities for specific use cases
But you may find that you have limited flexibility here, along with difficulty scaling. There is also significant rework required for system modifications.
Best Practices for Agent Router Implementation
Once you know which implementation approach will work for you, here are some best practices we recommend.
Scope Management: This is really crucial for success. Maintain focused and limited scope for router components. Breaking complex tasks into smaller, manageable skills will help your LLM execute tasks correctly. This modular approach ensures that each component has a clear, single responsibility.
Develop Clear Guidelines: Develop comprehensive function calling guidelines and explicit router definitions. Well-documented tool descriptions significantly enhance function call accuracy and system maintainability. This documentation serves as a crucial reference for both development and maintenance phases.
Performance & Monitoring: Implement robust monitoring solutions to track router performance and system behavior. Tools like Phoenix can provide detailed visibility into application operations, helping identify optimization opportunities and potential issues before they impact users.
An effective agent router is fundamental to building robust AI systems that can handle diverse user requests efficiently. By carefully considering implementation approaches and following established best practices, you can create a routing system that balances flexibility, performance, and maintainability. Regular monitoring and iterative improvements will ensure your agent router continues to meet your system’s evolving needs.
Want to monitor and optimize your agent’s performance? Try Arize Phoenix for in-depth visibility into your agent’s decisions.