Agentic AI has emerged as a game-changer, with the potential to fundamentally transform how organizations operate and scale. Agentic systems are designed to break down complex objectives into subtasks, then select tools, execute actions and iterate based on outcomes—often without human review at each step. This shift from static generation to iterative execution introduces a new class of requirements businesses must consider, including reliability, cost predictability, low latency, explainability and security under constrained scope. While general-purpose models were designed to maximize versatility, I have found in my work with agentic systems that these models demand the opposite: controlled capability within a defined domain. This is leading more companies to adopt a narrow LLM system instead of relying solely on general-purpose LLMs.

Full opinion : How Narrow LLMs Are Powering Agentic AI Systems.