Multi-Agent RAG Pipeline Optimization: Sub-Agent Routing & Context Pruning

As enterprise knowledge bases scale into millions of documents and heterogeneous code repositories, simple naive RAG (Retrieval-Augmented Generation) suffers from severe retrieval degradation. Standard top-k cosine similarity queries often return irrelevant chunks that dilute prompt context windows and spike API token billing.

Figure 1: Router agent dispatching sub-queries across specialized vector indices and AST code graphs.

1. Sub-Agent Router Architecture

Instead of executing a single monolith vector query, modern multi-agent RAG architectures employ a fast, low-latency Router Sub-Agent (e.g. using Claude 3.5 Haiku or fine-tuned Llama 3) to analyze user intent and decompose requests into specialized sub-queries:

// Example Router Dispatcher Payload
{
  "original_query": "How does our auth system handle OAuth2 token refreshing?",
  "sub_tasks": [
    { "target_index": "codebase_ast", "query": "OAuth2 refresh token handler function signature" },
    { "target_index": "api_docs", "query": "POST /api/v1/auth/refresh contract" }
  ]
}

2. Hybrid Retrieval with Cohere Rerank v3

Combining dense embeddings (e.g. OpenAI text-embedding-3-large) with sparse keyword retrieval (BM25) and applying Cohere Rerank v3 filters out up to 80% of uninformative context prior to main agent synthesis: