How do you build a multi-agent agentic RAG system?

Multi-agent agentic RAG uses a shared vector memory store where independent agents collaborate, each handling specialized tasks.

Architecture:

Agent 1 (Supply Chain) Agent 2 (Compliance) Agent 3 (Finance) ↓ ↓ ↓ └───────────────────────────┴──────────────────────────┘ Shared Milvus Vector Store (embeddings, metadata, documents)

Key patterns:

1. Shared collections: All agents query the same Milvus collections. One agent’s retrieval informs another’s reasoning.

2. Agent-specific metadata: Each agent filters by role-specific metadata ("supply_chain=true", “compliance=true”). Same data store, isolated queries.

3. Cross-agent handoff: Agent 1 retrieves supplier data, passes document IDs to Agent 2, which enriches the data with compliance checks. No re-indexing needed.

4. Consistency guarantees: Milvus’s ACID semantics ensure all agents read consistent data. Critical for financial workflows.

5. Scalability: Single vector database scales to 1000s of agents querying simultaneously. Faster than federated systems.

Use cases:

• Supply chain + finance agents collaborate to optimize procurement
• Customer support agents (billing, technical, retention) share customer vector profiles
• Legal agents (contract review, regulatory) query shared case law embeddings

Milvus enables this shared-memory pattern natively. No additional orchestration layer needed.

Related Resources:

• Agentic RAG with LangGraph
• Milvus overview
• Enhance RAG performance