As enterprises rapidly adopt AI, it’s important to evaluate how models interact with data and execute tasks. Here’s a quick breakdown of three leading approaches:
🟣 Model Context Protocol (MCP):
A standardized protocol ensuring direct, real-time, secure access to dynamic data sources—without relying on vector storage. Ideal for low-latency enterprise applications.
🟧 Agentic AI:
Multiple intelligent agents coordinate via an orchestrator (or crew system). Offers flexibility but brings high latency, resource intensiveness, and debugging challenges.
🩷 Retrieval Augmented Generation (RAG):
Fetches context via embeddings from vector DBs before response generation. Powerful but suffers from static embeddings, semantic gaps, and context staleness.
💡 Each architecture has trade-offs. The future may not be “one size fits all” but a hybrid model that combines the best of each.
Which architecture do you think will dominate in enterprise AI adoption?
