Router - joehubert/ai-agent-design-patterns GitHub Wiki

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Architectural Patterns

Intent

The Router pattern analyzes incoming requests to determine their intent and directs them to specialized agents, tools, or workflows based on request classification, ensuring that each request is handled by the most appropriate component in an agentic AI system.

Also Known As

Intent Router, Dispatcher, Request Classifier, Task Allocator

Motivation

In complex agentic AI applications, different types of user requests require different handling approaches. For example:

  • Factual queries may need knowledge retrieval components
  • Creative tasks may need specialized generative agents
  • Analytical tasks may require specific tools or reasoning frameworks

Traditional monolithic approaches often struggle when:

  • A single LLM agent attempts to handle all possible user intents
  • System capabilities expand, making prompt engineering increasingly complex
  • Different requests require vastly different resources or response strategies

The Router pattern addresses these challenges by implementing a dedicated classification layer that determines the nature of incoming requests and directs them to specialized handlers, similar to how a network router directs packets to their appropriate destinations.

Applicability

When to use this pattern:

  • When your application needs to handle diverse user intents that require different processing approaches
  • When you have multiple specialized agents or tools that each excel at handling specific types of requests
  • When you want to optimize resource usage by deploying different-sized models based on task complexity
  • When your system needs to scale with new capabilities without requiring changes to existing components
  • When you need clear separation between request classification and request handling
  • When implementing a modular architecture where components can be developed and updated independently

Structure

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Components

The key elements participating in the pattern:

  • Request Analyzer: Examines incoming user requests to extract intent, domain, complexity, and other relevant metadata. May use classification models, keyword detection, pattern matching, or a lightweight LLM.

  • Routing Logic: Contains the decision-making rules that determine which specialized component should handle each classified request. May use rule-based systems, decision trees, or learned routing policies.

  • Specialized Handlers: Domain-specific agents, tools, or workflows that receive routed requests and process them according to their specialization. Each handler focuses on a specific type of request.

  • Context Manager: Maintains conversation history and user context across routing decisions, ensuring contextual continuity even as requests move between different handlers.

  • Fallback Handler: A general-purpose component that processes requests when no specialized handler is appropriate or available, ensuring the system always provides some response.

Interactions

How the components work together:

  1. The Request Analyzer receives an incoming user request and extracts key information about its intent, domain, and requirements.

  2. The Routing Logic evaluates the analyzed request against its routing rules and determines the most appropriate specialized handler.

  3. The Context Manager provides relevant historical context to the chosen handler.

  4. The selected Specialized Handler processes the request and generates a response.

  5. The response, along with updated context, is returned to the user and stored by the Context Manager for future interactions.

  6. If the Routing Logic cannot determine an appropriate handler, or if the chosen handler fails to process the request successfully, the Fallback Handler is activated to ensure the user receives some response.

Consequences

The results and trade-offs of using the pattern:

  • Benefits:

    • Improved response quality through specialized handling
    • Better resource utilization by matching request complexity to appropriate resources
    • Enhanced modularity, allowing system capabilities to expand without architectural redesign
    • Clearer system organization with explicit boundaries between different functional components
    • Easier maintenance as specialized handlers can be updated independently

  • Limitations:

    • Additional architectural complexity compared to monolithic approaches
    • Potential for misrouting if the Request Analyzer misclassifies the user intent
    • Overhead from the routing process that may impact response times
    • Possible context fragmentation when requests switch between different handlers
    • Challenge of maintaining consistent user experience across different handlers

  • Performance implications:

    • Small latency increase due to the additional routing step
    • Potential overall system efficiency gains through better resource allocation
    • Opportunity for parallel processing when multiple specialized handlers can work simultaneously

Implementation

Guidelines for implementing the pattern:

  1. Start with intent classification: Develop a taxonomy of request types your system needs to handle. Begin with broad categories and refine as needed.

  2. Design for graceful degradation: Ensure the system can still function if the routing component makes a mistake or if a specialized handler is unavailable.

  3. Balance granularity: Too few routing categories limits specialization benefits; too many creates maintenance complexity and potential for confusion.

  4. Consider hybrid approaches: Combine rule-based routing for clear-cut cases with ML-based classification for ambiguous requests.

  5. Monitor routing decisions: Implement logging and analytics to track routing patterns and identify areas for improvement.

  6. Handle multi-intent requests: Develop strategies for requests that contain multiple intents, either by breaking them down or routing to handlers capable of managing complexity.

  7. Manage context carefully: Ensure relevant conversation history and user preferences persist across different handlers.

  8. Implement feedback mechanisms: Allow handlers to indicate if they received a misrouted request to improve future routing decisions.

Code Examples

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Variations

Common modifications or adaptations of the basic pattern:

  • Hierarchical Router: Implements multiple layers of routing, with high-level routers directing to more specialized sub-routers for finer-grained classification.

  • Learning Router: Uses feedback from handler success/failure to continuously improve its routing decisions over time.

  • Confidence-Based Router: Routes requests based on confidence scores, potentially sending high-confidence requests directly to specialized handlers while routing ambiguous requests through additional classification steps.

  • Multi-Modal Router: Specialized for handling different input modalities (text, images, audio), directing each to appropriate processing pipelines.

  • Context-Aware Router: Incorporates conversation history and user preferences into the routing decision, not just the current request content.

Real-World Examples

Systems or applications where this pattern has been successfully applied:

  • Customer Support Systems: Routing customer inquiries to specialized agents based on product line, issue type, or technical complexity.

  • Large-Scale Assistant Platforms: Directing different user requests to specialized subsystems for calendar management, information retrieval, creative content generation, or computational tasks.

  • Enterprise Knowledge Management: Routing queries to different knowledge bases or processing pipelines based on the domain or department relevance.

  • Multi-Service Chatbots: Directing user requests to different backend services like booking systems, FAQs, or human agent queues based on intent detection.

Related Patterns

Other patterns that:

  • Planner: Often works in conjunction with Router, where the Router determines the domain and the Planner creates a detailed execution strategy.

  • Fallback Chains: Complementary pattern that provides alternative processing paths when primary routing fails.

  • Hierarchical Task Decomposition: May be used downstream from a Router to break complex tasks into manageable sub-tasks.

  • Complexity-Based Routing: Specialized Router variation that focuses specifically on computational complexity rather than domain or intent.

  • Multi-Agent: Router often serves as the entry point in a Multi-Agent system, directing requests to the appropriate specialized agents.