AI in South Korea is already moving beyond the experimental stage and is quickly becoming part of daily work. Data from the Korea Information Society Development Institute (KISDI) show 51.8 percent of Korean workers use generative AI for work purposes.

As a result, the challenge companies face is shifting from how much AI they adopt to how they make AI work inside their organisations. In particular, as AI evolves beyond assisting people to agents that plan and execute on their own, the shift from cloud native to AI native, and further to agent native, is demanding a redesign of work, decision-making and how organisations are run.

From infrastructure to behaviour

Cloud native, focused on efficiency in scaling and deployment, lowered software scaling costs and made deployment easier through elastic infrastructure, containers and microservices. AI native, by contrast, designs applications around models from the outset. These models interpret unrefined data and function as decision engines that automate everything from credit assessment to supply chain forecasting.

Agent native, meanwhile, is being defined as a stage in which agents combining language models, memory and tool-use capabilities plan and execute multi-step work across multiple corporate systems, while people focus on oversight and judgement. Existing cloud interfaces designed around human clicks are also shifting to skills and command-line tools that agents can directly understand and execute. Gartner forecast the share of enterprise applications equipped with agents for specific tasks will rise from under 5 percent in 2025 to 40 percent by the end of this year, and the number of agents per large company could reach tens of thousands by 2028. As agents are incorporated as new executors in companies, operating structures and management methods will inevitably change.

The changes executives need to watch fall into three broad areas. First, corporate digital transformation is shifting from "systems of record" to "systems of action". Agent-based AI shortens the time it takes to detect, decide and act across work, from fraud detection to executing small campaigns and adjusting inventory movement routes. Companies that adopted this cycle early are trying to adjust through software-led strategies rather than reorganising their organisations.

Workforce operations are also changing. Some companies are discussing recognising agents, given email addresses, system credentials and clearly defined responsibilities and working under human oversight, as "digital human employees". This could raise issues over accountability and audits, but it opens a way to expand organisational capabilities without increasing headcount at the same rate.

Finally, the criteria for choosing between building and buying are changing. As AI sharply lowers the cost of implementing customised software and work automation, companies are re-examining ways to implement tasks directly with agents that had relied on integrated software-as-a-service (SaaS).

What Korean companies should focus on

These changes must be reviewed in domestic companies alongside practical conditions such as regulation, security and data management. With the AI Basic Act taking effect in January this year, risk management for high-impact AI and human management and oversight have become legal requirements. The greater an agent's autonomy, the more a company must be able to track who executed what, under which authority. That is why runtime sandboxes, ID management and task-level security controls matter.

Where data are processed and managed is also key. Because agents perform work by directly accessing internal systems and customer data, companies need infrastructure that meets domestic regulations and data sovereignty requirements. Alibaba Cloud opened its third domestic data centre in August and expanded in-country foundations spanning agent development through operations and security because demand is growing for infrastructure to run AI agents in real corporate environments.

That is why a shift to agent native requires careful design rather than grand declarations. Executives have three tasks to carry out.

First is to design governance upfront. Human review and judgement, audit records, and enterprise ID and access control systems must be placed at the core from the initial stage of adoption. They are not elements that can be added later for compliance.

Next is building an agent platform layer. Companies need a common framework that standardises how agents access data, tools and credentials, and safely coordinates work instead of relying on customised, department-by-department integrations.

Finally, companies should treat implementation speed as a competitive variable. Competitive advantage comes less from a specific model itself than from how quickly companies connect agents to real work processes and continuously improve them.

Executives do not need to become AI theorists. But they should view agents as an organisational unit and apply the same principles of strategy, risk management and performance measurement used for teams of people. If AI agents are changing how companies work, executives must also change how they run their organisations.