Agentic Routing: Dynamic Context Selection in Complex RAG Pipelines

In simple Retrieval-Augmented Generation (RAG) applications, every query is routed through a single vector database. While this works for homogeneous datasets, it fails when applications must interact with diverse data sources.

For example, a user asking for "Q3 sales figures" requires a SQL query over a database. A user asking for "our company's policy on remote work" requires a semantic search over a vector database. A user asking for "the weather in San Francisco today" requires a web search tool.

Agentic Routing solves this by using LLM-based logic to dynamically determine the best destination or tool for each query.

Agentic Query Routing Architecture

The Architecture of Agentic Routing

An agentic router behaves as a traffic controller:

1. It receives a query.
2. It evaluates the query against metadata descriptions of available routes.
3. It selects the optimal route (or set of routes).
4. It calls the target system, aggregates the result, and returns it to the generator.

The routing decision can be formulated as a classification function f(q):

f(q) = c \in \{C_1, C_2, \dots, C_k\}

where each C_i represents a distinct retrieval channel. This is typically achieved using LLM function calling or structured JSON outputs.

Python Implementation of an Agentic Router

Here is an implementation of a dynamic router using Pydantic and OpenAI's structured outputs:

from pydantic import BaseModel, Field
from typing import Literal
import openai

# Define the routing target schema
class RouteChoice(BaseModel):
    target: Literal["vector_db", "sql_db", "web_search", "direct_response"] = Field(
        description="The best data source to resolve the user query."
    )
    rationale: str = Field(description="Brief explanation for the choice.")

def route_query(query: str) -> RouteChoice:
    client = openai.OpenAI()
    
    system_prompt = "You are a routing agent for a query processing system."
    
    response = client.beta.chat.completions.parse(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": query}
        ],
        response_format=RouteChoice,
        temperature=0.0
    )
    return response.choices[0].message.parsed

# Test cases
# choice = route_query("What is the average transaction size in our sales table?")
# print(f"Route: {choice.target}")

Implementing Fallback Routing

In production systems, routing can also operate in a multi-hop or fallback configuration:

• If a vector search retrieves zero-score results, the router can automatically fallback to a web search or request user clarification.
• If a SQL query fails due to syntax errors, the router can feed the error back to the LLM to rewrite the query.

Conclusion

Agentic routing is crucial for building multi-source AI applications. By routing queries dynamically based on semantic intent, you minimize database load, access real-time APIs, and ensure the LLM receives the precise context needed to answer the user's prompt.