Direct queries to specialized handlers based on intent classification and conditional logic
Learn how to intelligently direct queries to specialized handlers based on intent classification and conditional logic.
Routing Diagram
Topic: routing
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Routing is a design pattern that directs user queries to specialized handlers based on intent classification, enabling efficient task delegation to domain-specific agents.
Instead of one agent handling all queries, routing allows specialized agents to focus on their expertise, improving accuracy, performance, and maintainability.
Use routing when you have clearly distinct query types that benefit from specialized handling, and when intent boundaries are well-defined with minimal overlap.
Simple Analogy: Think of routing like a receptionist at a large company. When someone calls with a question, the receptionist doesn't answer everything themselves. Instead, they listen to what you need and transfer you to the right department - sales, support, billing, or technical help. That's exactly what routing does for AI agents!
Routing is a design pattern where an AI system analyzes an incoming request and decides which specialized handler should process it. Instead of having one agent try to do everything, you create multiple specialized agents and use routing to send each request to the right expert.
Key Components:
There are four primary mechanisms for implementing routing in AI agents, each with different trade-offs:
Uses a language model to analyze the query and decide which route to take. Most flexible but higher latency and cost.
Converts queries to embeddings and uses similarity search to find the best route. Fast and cost-effective for semantic matching.
Uses keyword matching, regex patterns, or conditional logic. Fastest and most predictable but less flexible.
Uses a trained classification model (e.g., BERT, DistilBERT) for intent detection. Balanced accuracy and speed.
The system analyzes the user's input to understand what they're asking for. This can be done using keyword matching, embeddings similarity, or an LLM classifier.
Based on the classification, the router selects the appropriate handler. This is typically done using conditional logic or a routing table.
The selected handler processes the request using its specialized knowledge and tools, then returns a response.
The handler's response is returned to the user, completing the routing cycle. The user never sees the routing logic - just the result.
When your application handles multiple distinct domains (e.g., customer service with sales, support, and billing)
When different types of queries require different tools, knowledge bases, or processing logic
When you want to use lighter, faster models for simple queries and reserve powerful models for complex ones
When user intents can be clearly categorized into distinct groups with minimal overlap
Analyze query to determine user intent and category
Map intent to appropriate specialized handler
Specialized agent processes query with domain expertise
Deliver specialized response back to user
Specialization improves accuracy: Routing enables domain-specific agents to handle queries they're optimized for, resulting in better responses.
Choose the right mechanism: LLM-based for flexibility, embedding-based for semantic matching, rule-based for speed, ML-based for balance.
Always have a default route: Handle edge cases and unclassified queries gracefully with a fallback handler.
Monitor routing accuracy: Track classification confidence and misrouted queries to continuously improve your routing logic.
Consider hybrid approaches: Combine multiple routing mechanisms (e.g., rule-based for obvious cases, LLM-based for ambiguous ones) for optimal performance.
Test with real queries: Use actual user queries to validate routing accuracy and identify edge cases that need special handling.
