The Age of Self-Working AI: Agentic AI

If generative AI is a tool that produces results tailored to a given request, agentic AI is an AI agent that understands a goal and decides its next actions on its own, executing them in sequence. It doesn't stop at simply producing an answer—it also designs internally how to solve the problem.

The key drivers are reasoning ability and tool-use ability. A reasoning model follows a Plan → Act → Observe flow: it sets a plan, calls tools to execute it, then looks at the results and revises its path. When needed, it uses external tools such as web search, internal document lookup, or a calculator to make up for the limits of its internal knowledge.

This approach greatly expands the range of tasks AI can handle. As it becomes possible for AI to break down complex problems on its own, find the information it needs, verify results, and revise again, AI comes closer to being a colleague that carries actual work forward in areas such as disaster response, space exploration, coding, customer service, and healthcare.

Team Naver is applying this approach across multiple services, including search, shopping, webtoons, and maps. For example, when the shopping agent receives a request like "buy groceries within this week's food budget," it goes on to create an ingredient list, compare prices and deals, and narrow down options that fit the conditions, then even follows up with menu recommendations after the purchase.

However, as autonomy grows, transparency and safety become more important. Trust is built only when the AI can explain why it made a particular choice, and tasks that lead to real-world actions—such as purchasing, booking, posting, or deleting—require a human confirmation step. In the end, the challenge for agentic AI is to increase autonomy while clearly preserving human control and accountability.