Agentic RAG: Routing Queries to Specialized Agents for Better Information Retrieval

Retrieval-Augmented Generation (RAG) has become the standard architecture for grounding LLM responses in factual, up-to-date information. But vanilla RAG—embed a query, retrieve documents, generate a response—quickly hits its limits with complex information needs.

Consider a query like "I have chest pain and shortness of breath, should I see a cardiologist near me?" This requires understanding symptoms, connecting them to conditions, and finding relevant providers—three distinct capabilities that no single retrieval strategy optimizes for.

This post explores the concepts behind agentic RAG systems that route queries to specialized agents, each equipped with domain-specific retrieval strategies.

The Limits of Monolithic RAG

Consider three different types of queries:

"What causes high blood pressure?"
"Compare symptoms of anxiety vs heart attack"
"Find a cardiologist in Seattle who accepts new patients"

A single RAG pipeline struggles here:

Query 1 needs encyclopedic content retrieval
Query 2 requires structured comparison across multiple topics
Query 3 needs provider search with geographic and availability filtering—a fundamentally different index and query structure

Each query type benefits from different retrieval strategies, ranking approaches, and response formats. The agentic approach solves this by routing each query to an agent designed for that query type.

Agentic RAG Architecture

The core idea is simple: a supervisor agent classifies incoming queries and routes them to specialized sub-agents, each optimized for a particular query type.

User Query
    │
    ▼
┌──────────────────────────┐
│    SUPERVISOR AGENT      │
│                          │
│  • Classifies intent     │
│  • Routes to specialist  │
│  • Handles fallbacks     │
└──────────────────────────┘
    │
    ├──────────┬──────────┬──────────┐
    ▼          ▼          ▼          ▼
┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐
│ Agent  │ │ Agent  │ │ Agent  │ │ Agent  │
│   A    │ │   B    │ │   C    │ │   D    │
└────────┘ └────────┘ └────────┘ └────────┘
    │          │          │          │
    ▼          ▼          ▼          ▼
 Tool A    Tool B     Tool C     Tool D
(Hybrid   (Content   (Provider  (Service
 Search)   Search)    Search)    Catalog)
    │          │          │          │
    └──────────┴──────────┴──────────┘
                    │
                    ▼
           Response Generation
                    │
                    ▼
           Grounded Response

The Supervisor Agent

The supervisor is the entry point. It classifies incoming queries and routes them to the appropriate specialist.

Intent Classification

Rather than training a traditional ML classifier (which requires labeled data and ongoing maintenance), modern agentic systems often use the LLM itself for classification. A well-crafted prompt can classify query intent with high accuracy:

The classification prompt typically:

Presents the query
Defines available categories with clear descriptions
Asks for structured output (category + confidence score)
Uses low temperature (0.1-0.2) for consistent classification

Low temperature is important—the same query type should route the same way every time. Classification should be deterministic.

Routing Logic

The supervisor routes based on classification confidence:

High confidence (>0.8): Route directly to the classified agent
Medium confidence (0.5-0.8): Route but flag uncertainty in metadata
Low confidence (<0.5): Use a general-purpose fallback agent

This graceful degradation ensures the system always responds, even for ambiguous queries.

Forced Routing

Sometimes the UI context should override classification. If a user clicks "Find a Doctor" button and types a query, it should route to the provider search agent regardless of query content. This "forced routing" pattern lets UI context inform agent selection.

Multi-Intent Queries

Some queries contain multiple intents: "What are symptoms of diabetes and can you find me an endocrinologist?"

The supervisor can detect and handle these by:

Splitting the query into sub-queries
Routing each sub-query to the appropriate agent
Combining responses into a coherent answer

This adds complexity but significantly improves handling of natural compound queries.

Specialized Agents

Each agent is optimized for its query type with appropriate retrieval strategies and response formats.

General Question-Answering Agent

The broadest category—general questions seeking information. This agent typically uses:

Hybrid search combining keyword and semantic retrieval
Reranking for precision in the top results
MMR diversity to ensure comprehensive coverage
Multi-field search across titles, abstracts, and content

The response format emphasizes clear explanations with source attribution.

Condition/Topic Description Agent

For queries requesting comprehensive information about a specific topic, the agent needs:

Higher diversity in retrieval to cover all aspects (symptoms, causes, treatments, etc.)
Structured response format organizing information into logical sections
Broader retrieval to gather information from multiple sources

The response format might include distinct sections for overview, details, related topics—presenting information in a structured, scannable way.

Provider/Entity Search Agent

Finding specific entities (providers, locations, products) requires fundamentally different retrieval:

Structured filters for attributes (specialty, location, availability)
Geographic queries for location-based matching
Faceted search for refining results

The response includes structured data (names, addresses, availability) rather than prose explanations.

Service/Catalog Agent

For queries about what's available, this agent might:

Search a service catalog rather than content index
Return structured lists of options
Include filtering and comparison capabilities

Retrieval Tool Design

Each agent uses retrieval tools optimized for its needs:

Hybrid Search Tool

Combines lexical and semantic retrieval:

BM25 for keyword matching
Vector search for semantic similarity
RRF for score fusion
Configurable parameters for precision vs recall tradeoff

Content Search with MMR

For comprehensive information needs:

Higher candidate count for initial retrieval
Lower lambda parameter for MMR diversity
Section-level matching for long documents

Provider Search with Geographic Filtering

For entity discovery:

Geo-distance filtering and scoring
Faceted search on structured attributes
Distance decay for ranking closer results higher

Response Generation

All agents feed retrieved content to the LLM for response generation. Key principles:

Grounding

The response should be based on retrieved content, not the LLM's parametric knowledge. This means:

Explicitly instructing the model to use only provided sources
Requiring source attribution
Handling cases where sources don't contain sufficient information

Domain-Appropriate Hedging

For sensitive domains, responses should include appropriate caveats. The generation prompt should specify:

When to recommend professional consultation
What claims to avoid making
How to handle uncertainty

Structured Output

Rather than just text, agents often return structured data:

Main answer text
Source citations
Related questions (FAQ)
Suggested next actions
Structured content (for condition descriptions, provider results, etc.)

This enables rich UI rendering beyond simple text display.

Error Handling and Fallbacks

Production systems need robust error handling at multiple levels:

Classification Failures

If the supervisor fails to classify (API error, parsing error), fall back to the general question-answering agent.

Agent Failures

If a specialized agent fails:

Log the error with context
Attempt simpler retrieval
Generate a graceful degradation response
Include error metadata for debugging

Retrieval Failures

If retrieval returns no results:

Try broader search parameters
Suggest query refinements
Acknowledge the limitation rather than hallucinating

The system should always respond with something useful, even during partial failures.

Performance Characteristics

Agentic architecture adds routing overhead but enables per-query-type optimization:

Query Type	Routing	Retrieval	Reranking	Generation	Typical Total
General Q&A	~100ms	~80ms	~300ms	~400ms	~900ms
Comprehensive topic	~100ms	~100ms	~400ms	~600ms	~1200ms
Entity/provider search	~100ms	~60ms	-	~300ms	~500ms
Simple autocomplete	-	~30ms	-	-	~50ms

Entity search is faster because it doesn't use neural reranking (rankings are based on structured data). Comprehensive topic queries are slower because they generate more extensive responses.

Deployment Considerations

Stateless Design

Agents should be stateless—all context comes from the request. This enables:

Horizontal scaling
Serverless deployment
Easy testing and debugging

Observability

Each agent should emit structured logs including:

Query characteristics (length, type, confidence)
Routing decisions
Retrieval metrics (count, latency)
Response characteristics
Error information

This enables dashboards tracking agent performance, routing distribution, and error rates.

A/B Testing

The agentic architecture makes it easy to test improvements:

Route a percentage of traffic to new agent versions
Compare metrics between agent implementations
Gradually roll out improvements

Key Takeaways

The agentic RAG architecture transforms a single-purpose retrieval system into a flexible assistant capable of handling diverse information needs:

Supervisor routing enables specialized handling without requiring query-type detection in the UI
Specialized agents optimize retrieval strategy for each query type
Tool-based retrieval lets agents use different indices and query patterns
Structured responses enable rich UI rendering beyond simple text
Graceful fallbacks ensure the system always responds, even during partial failures

This architecture pattern extends beyond any specific domain. Any application with diverse query types and the need for specialized retrieval strategies can benefit from the supervisor + specialized agents approach. The key is identifying distinct query intents and optimizing each agent's retrieval pipeline for its specific use case.

This post explores architectural concepts. For implementation, consider frameworks like LangChain, LlamaIndex, or custom implementations using your preferred LLM provider's API. The LangGraph documentation provides good examples of agent orchestration patterns.

Note: The patterns discussed here are intentionally generalized, drawn from industry experience but presented as transferable concepts rather than specific proprietary implementations.