MCP for the enterprise is a standardized protocol that grounds LLMs with real-time company data and uses agentic RAG to generate more contextual responses.
The origins of MCP for the enterprise
The emergence of sophisticated generative AI (GenAI) frameworks, such as Retrieval-Augmented Generation (RAG), and agentic RAG systems powered by Large Language Models (LLMs), promises a new era of intelligent business automation.
A RAG architecture LLM agent is capable of chain-of-thought reasoning, decision-making, and acting. However, ensuring the appropriateness, accuracy, and safety of its answers and actions remains a key concern.
A novel framework for addressing this challenge is MCP AI, which relies on the integration of enterprise data and a contextual understanding of the question to be answered, or the action to be taken, to guide and validate the behavior of LLM agents. Only by understanding context can MCP provide for accurate and secure LLM responses to customer queries, and enable effective agentic AI systems for autonomous seller-buyer interaction.
MCP can be seen as an evolving set of principles and practices born from the growing need to control and understand the behavior of GenAI models, particularly those with autonomous capabilities. Its origins can be traced back to several key areas, such as:
- Explainable AI (XAI)
As AI models became black boxes, the demand for transparency grew. MCP builds upon XAI principles by aiming to make the reasoning and decision-making processes of agentic RAG more understandable and verifiable.
- Verification and Validation (V&V) in software engineering
Traditional software development methodologies have long emphasized the importance of testing and validation to ensure that the software functions as intended. MCP adopts these concepts, applying them to the unique challenges posed by LLM-powered autonomous agents.
- Control theory and robotics
The field of robotics has grappled with the need to control autonomous systems for decades. MCP draws inspiration from control theory, focusing on establishing boundaries and feedback mechanisms for guiding agent behavior within acceptable parameters.
- Knowledge graphs and semantic understanding
Early attempts to ground AI relied heavily on structured data. MCP acknowledges the need for structured and unstructured data, emphasizing the importance of RAG architecture.
MCP was developed because simply training an LLM and equipping it with agents, does not generate enough context to respond to real-world situations. Without a clear understanding of concext, the potential for unexpected consequences, biases, and AI hallucinations is greatly increased.
MCP enterprise challenges
MCP enterprise deployments must address several key challenges facing agentic RAG systems, including:
- Credibility
LLMs, while powerful in language generation, can sometimes operate without a firm connection to real-world facts and enterprise-specific data. This confusion can lead to generative AI hallucinations or actions based on inaccurate or irrelevant information.
- Insufficient evidence
The decision-making processes of complex LLMs can be difficult to trace and understand. This lack of transparency makes it challenging to identify the reasons behind an agent's actions and to debug potential issues.
- Behavioral problems
Without clear conceptual boundaries and control mechanisms, agentic AI systems can exhibit unexpected or undesirable behavior, especially in novel or edge-case scenarios.
- Performance reviews
Traditional testing methods may not be sufficient to thoroughly validate the behavior of autonomous agents that can learn and adapt over time. Ensuring that an agent consistently operates within acceptable ethical and operational boundaries requires a more nuanced approach.
- Enterprise integration
Agentic RAG needs to interact with existing enterprise apps and data sources. Ensuring integration while maintaining data integrity and security is a significant challenge.