How context-aware agents and open protocols drive real-world success in enterprise AI

Artificial intelligence is moving from experimentation to operational deployment. The excitement around large language models (LLMs) introduced many organizations to what AI could do, sparking a wave of pilots and prototype agents.

But as enterprises push these systems into production, they're encountering a fundamental constraint: general-purpose models lack the real-time operational context that enterprise decisions require.

LLMs are remarkable, but they were built for breadth, not depth. They excel at conversation and summarization, but they lack the real-time, domain-specific context on which enterprise decisions depend.

A chatbot can discuss financial regulations, but it cannot determine whether a specific trade violates internal policy. It can describe networking concepts, but it cannot diagnose why your application is slow right now without live telemetry. Simply ...