Artificial intelligence is passing through another massive inflection point. While the past two years represented an era of "reactive intelligence" that answered questions, we are now entering the age of "agentic AI," where AI autonomously plans, reasons, and acts. This signifies more than algorithmic improvement—it marks a paradigm shift in computing from "generation" to "action," and from "tool" to "agent."
This trend is rapidly materializing in the corporate world. In January this year, the open-source agent execution framework "OpenClaw" was released, enabling anyone to create and scale agents. More recently, at Nvidia's GTC 2026, "NemoClaw" emerged with enhanced security and governance features, positioning agentic AI as the core of next-generation computing. This represents AI evolving into a new "Action OS" through agentic platforms.
Examining this in detail, the starting point is the "harness"—the execution framework that wraps around agents. It assigns roles and permissions to agents, breaks down given objectives into subtasks, and executes them step by step. "Collaboration tools" are built on this foundation, coordinating multiple agents and integrating with existing business systems. While these elements enable agents to function, "skills" are the core competitive differentiator—the ability to convert industry-specific knowledge and decision criteria into executable agent actions. The deeper the integration of field expertise, the greater the differentiation that generic models cannot easily replicate.
The evolution of agentic AI will cause tectonic shifts across economic structures. From the "transaction cost" perspective of Nobel economist Ronald Coase, agentic AI will dramatically lower the marginal costs of business activities and exponentially increase speed and productivity by automating search, negotiation, and decision-making processes. An "Agentic Economy" where humans and AI conduct economic activities together is forming.
In this global agentic AI competition, Korea's strategy must be preemptive and structural. First, we must secure "agent infrastructure sovereignty." Beyond simply utilizing global big tech models, we need to develop our own agent operating platforms, safely leverage our unique data, and build infrastructure combining our neural processing unit (NPU) and AI semiconductor capabilities.
Second, Security by Design for agents must become standard practice. As agentic AI directly controls core enterprise systems, security has penetrated deeper than ever. We must strengthen research and development in "agent guardrails" alongside legal and technical guidelines that clarify permission management, behavior tracking, and accountability structures in case of malfunction.
Third, we must cultivate industry-specific agentic AI ecosystems. We need to combine specialized knowledge from our competitive sectors—manufacturing, healthcare, and finance—with agentic AI's "tool utilization" capabilities. Beyond generic models, we should establish leading cases and global standards in "execution-oriented AI" that solves real problems on industrial frontlines.
If the internet transformed information flow and mobile changed connectivity methods, agentic AI is a transformation that will reshape the execution layer of the economy. In this massive transition period, leading the era of "intelligent action" will form the solid foundation for becoming an AI G3 nation.
