Adding MCP to Agentic RAG Systems: A Comprehensive Guide

If you are building Retrieval-Augmented Generation (RAG) systems and incorporating multiple data sources for retrieval, there is likely some level of agency involved, at least in the data source selection stage. This is where MCP (Multi-Component Platform) comes in, significantly enhancing the evolution of your Agentic RAG systems.

How MCP Enriches Agentic RAG Systems

Here’s a detailed breakdown of how MCP integrates into and improves your Agentic RAG systems:

1. Analysis of the User Query

The process begins with passing the original user query to an LLM-based Agent for analysis. During this stage:

  • The original query can be rewritten, sometimes multiple times, to create either a single or multiple queries for the pipeline.
  • The agent determines if additional data sources are required to answer the query.

2. Retrieval Step

If additional data is needed, the retrieval step is triggered. MCP allows access to a variety of data types, including:

  • Real-time user data
  • Internal documents that may be relevant to the user
  • Web-based data

This flexibility ensures comprehensive data retrieval for accurate responses.

MCP’s Key Benefits

Integrating MCP into your Agentic RAG systems offers several advantages:

  • Decentralized Management: Each data domain can manage its own MCP Servers, enforcing specific rules for data usage.
  • Enhanced Security: Security and compliance are ensured at the server level for each domain.
  • Scalability: New data domains can be easily added to the MCP server pool in a standardized way, requiring no agent rewrite. This enables decoupled evolution of the system in terms of Procedural, Episodic, and Semantic Memory.
  • Standardized Data Exposure: Platform builders can expose their data in a standardized way to external consumers, facilitating easy access to web data.
  • Focus on Agent Topology: AI Engineers can concentrate on the topology of the Agent, leaving data management to MCP.

3. Data Consolidation and Reranking

Retrieved data is consolidated and reranked using a more powerful model compared to a regular embedder. This significantly narrows down the data points, ensuring relevance and accuracy.

4. Answer Composition

If no additional data is required, the system attempts to compose the answer (or multiple answers/actions) directly via an LLM.

5. Answer Analysis and Evaluation

The answer undergoes analysis, summarization, and evaluation for correctness and relevance:

  • If the Agent deems the answer satisfactory, it is returned to the user.
  • If improvement is needed, the user query is rewritten, and the generation loop is repeated.

Conclusion

MCP is a game-changer for Agentic RAG systems, offering enhanced data management, security, and scalability. By integrating MCP, you can focus on building robust, reliable, and introspective agents capable of handling real business processes. Have you implemented MCP in your systems? Share your experiences in the comments below!

Learn more about building such systems in my End-to-End AI Engineering Bootcamp.

Tags: #LLM #AI #MachineLearning