Originally published on tamiz.pro.
You built a prototype. The model connected to the server, fetched a tool, and the loop closed perfectly. But when you pushed to production, the agents started hallucinating context, timing out on I/O, or ignoring constraints entirely. If this sounds familiar, the problem isn't your model—it's your architecture.
The current wave of AI agent development is obsessed with the Model Context Protocol (MCP). It offers a standardized way to expose tools and resources to LLMs, solving the fragmentation problem that plagued early RAG systems. However, a growing chasm exists between local demo success and enterprise-grade reliability.
This article argues that the failures we see in production aren't bugs in the protocol itself, but symptoms of treating an asynchronous, stateful, resource-heavy protocol as if it were a synchronous REST API.
The "It Works on My Machine" Syndrome
