AI Summit Seoul & Expo 2026 ran August 19–21 at COEX in Seoul. 118 companies from seven countries took 307 booths, and Enhans was there with a booth and two speaking sessions.

At the booth we demoed AgentOS and ACT-2. In the two sessions we covered the ontology layer, and the purpose and measurement of agentic AI.

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The questions on the floor had changed this year

Over three days, about 2,000 people came to the Enhans booth. The interest on the floor was less in what AI can do and more in which work to hand it, and what results that work should produce.

Model performance mattered. So did how AI connects to the systems a company already runs, how much of today's work it can take on, and what changes after deployment. Agentic AI and industry adoption ran through the whole event the same way.

The message we brought to the show sits in the same place.

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AI for Real Work.

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Does AI doing a company's work simply mean automating more of it? We think two questions come first. What is the work for? And how do you measure what it produced?

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An automation rate doesn't explain results

The first number in any conversation about enterprise AI adoption is the automation rate. 70%, 80%, 90% of the work handled by AI. It's easy to say and easy to picture.

But if the automated work isn't connected to the problem the company actually has to solve, that number alone doesn't explain business results.

In his August 20 conference session, Seunghyun Lee, CEO of Enhans, laid out two problems agentic AI has to get past: automation without purpose, and improvement without measurement.

The talk also named where AI projects fail. Solving a badly defined problem quickly. Optimizing a proxy metric that sits far from business results. Building a model that doesn't fit how the work actually runs.

So the starting point for Real Work isn't how much to automate. It's defining what the automation is for.

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Give an agent a purpose, not a task list

Take procurement.

"Automate 80% of the procurement process" looks like a goal. But automation itself isn't what a procurement team wants. They want the parts they need on time so production doesn't stop and revenue isn't lost, and they want fewer unnecessary purchases and lower total procurement costs. That's closer to why the function exists at all.

In the session we defined that as a specific Purpose: reduce unnecessary purchases and total procurement costs, while lowering production stoppages and lost revenue from supply disruption.

That difference changes the basis for judgment. An agent repeating a set procedure quickly and an agent reading a situation against what the company is trying to achieve will make different decisions under the same conditions.

Real Work doesn't start from a task list. It starts from understanding the business purpose.

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No purpose without the company's language

To understand the purpose, an agent has to know the company's language. A model can know a great deal in general and still not know one company's product taxonomy, trading relationships, operating rules, or judgment criteria. When enterprise data sits across SAP, ERP and CRM and each system uses different terms, the same customer and the same product exist under different names.

That's why our Expo session covered data and the ontology layer together. The Ontology Manager in AgentOS defines the entities, relationships, rules and terms inside enterprise data, and moves that data into business meaning.

Agents judge a situation against that structure. They work from the company's operating logic, not from an answer that just sounds plausible.

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Then measure the value that work created

Once the purpose is set, the next question gets clearer. What business value did this agent create?

Enhans doesn't judge AI by volume processed or speed. The session put it in one sentence: "KPI must be tied with business impact."

In procurement, that means looking past the savings figure to demand reduction, total cost of ownership (TCO), supply stability and procurement lead time. Those results feed into business impact: cost savings, cost avoidance, revenue loss prevented, incremental revenue, working capital effects.

Add the cost of running the AI, the human management cost and the expected risk, and you can talk about the net value agentic AI created.

The measure isn't how much AI you used. It's what AI changed in the company's numbers.

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When agents outnumber the people in a company

The number of AI agents working inside a company keeps growing. Instead of one agent doing everything, it looks more like separate agents for procurement, finance, sales, marketing and research, each with its own purpose, working alongside people.

Enhans expects a point where AI agents outnumber the people inside a company. For that many agents to move toward the company's purpose and work with people, something new has to connect and run them.

AgentOS started there. It connects scattered data, structures work context with ontology, and agents judge against that context. ACT-2, our Computer-Use Agent, takes those judgments into the live web and enterprise systems and carries the work through to the end.

People still decide. Setting the goal, the direction and the limits is a person's job. The agent turns that decision into work that gets done.

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You Decide. Our Agents Act.

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AI for Real Work

AI Summit Seoul & Expo 2026 confirmed again that AI is moving from a technology that answers to a technology that does the work.

But doing the work isn't enough on its own. It has to understand exactly what problem the company needs to solve, act with a clear purpose, and let you check whether the result turned into business value.

That's where Enhans puts AI for Real Work. Not AI that handles more, but AI that understands a company's purpose, does the actual work, and lets you confirm that value in numbers.