Agent Engineering for the Enterprise: An MCP-Based Framework

The rapid evolution of artificial intelligence has driven organizations across sectors to develop agents that genuinely augment human expertise and automate complex tasks. Retrieval‑Augmented Generation (RAG) has proven to be a promising paradigm: it unites large language models with external knowledge retrieval to boost factual accuracy and domain relevance. However, the seamless integration of heterogeneous tools and context sources remains a thorny challenge. The Model Context Protocol (MCP) is a lightweight and extensible framework designed to unify the exchange of structured context between RAG‑powered agents and external services. MCP defines a clear JSON schema for context requests and responses—encompassing metadata, user session state, and tool interfaces. In particular, we found that standardizing these exchanges simplifies the orchestration of multi‑modal capabilities, whether database queries, knowledge‑base lookups, or custom computations. MCP establishes a unified and extensible standard for context exchange, aimed at simplifying integration of AI agents with heterogeneous tools and services. By clearly defining request and response schemas, it reduces implementation ambiguity and promotes consistency between teams. The modular design of the protocol facilitates interoperability, accelerates development cycles, and reduces maintenance overhead, also helping enforce governance policies and scalable, stateful agent interactions. Ultimately, MCP provides a solid foundation for building reliable AI solutions in dynamic enterprise environments.