As AI systems move from experimentation to production, developers are starting to discover a new problem: The tools that large language models (LLMs) depend on do not scale well when they run on a single laptop. Early agent prototypes usually start with a simple local Model Context Protocol (MCP) server, which is perfect when you are exploring ideas, but these setups break quickly once multiple teams or real workloads enter the picture.
I ran into this firsthand while building LLM-driven automation inside enterprise environments. Our early…
