AI agents don't change your toolset, but your way of working

In many organizations, AI is now part of daily work. Not as an innovation project, but as a reality. Employees use Copilot, ChatGPT, and other generative applications to write, analyze, and prepare faster. The first phase of adoption is behind us: AI as a smart assistant has been accepted.

But while organizations are still working on professionalizing this use, the next development is emerging that is fundamentally different. Not a better prompt. Not a smarter summary. But systems that act independently.

AI agents mark a tipping point. For the first time, AI is shifting from thinking along to executing. From support to action. And with that, it not only affects individual productivity, but the design of processes, responsibilities and organizational design.

In episode three of AI in Business , Femke Cornelissen (Chief Copilot & Agentic Workplace at Wortell) and Danny Burlage (Founder & CSO at Wortell) explore what this shift means in concrete terms. Not as a pipe dream, but as a development that is already visible in service desks, finance departments and operational teams.

The question is no longer whether AI is used. The question is: who or what carries out the work?

What distinguishes an agent from a chatbot

The term "agent" is widely used, but rarely sharply defined. In essence, an AI agent is software that is given a goal and then independently takes steps to achieve that goal. That seems like a nuance difference compared to a chatbot, but it is a fundamentally different category.

A traditional AI application responds to a prompt and generates output. An agent, on the other hand, can hold context, consult systems, make decisions within predetermined frameworks, and actually perform actions in other applications. Think of drawing up a change proposal, processing an order, analyzing invoices or coordinating an onboarding process.

This shifts AI from advice to implementation. And it is precisely this shift that makes agents relevant from an organizational point of view.

From personal productivity to process impact

The first wave of AI within organizations focused mainly on individual productivity. Meetings are summarized, emails are rewritten, and documents are checked. This provides demonstrable efficiency, but remains largely at the level of the individual employee.

Agents operate on a different level. They touch on processes, team responsibilities and even organizational structures. When an agent autonomously processes orders or draws up proposals for changes, not only does the speed of work change, but also the division of roles between people and technology.

This is aptly mentioned in the podcast: we are shifting from thinking in terms of tasks to thinking in terms of outcomes. The question is no longer who performs an action, but what result is needed and how it is best achieved: human, digital or in collaboration.

This requires a renewed design of processes.

The degree of autonomy determines the impact

An important distinction made in the episode is the extent to which people remain involved in agent-driven processes. Three levels are distinguished.

• In human in the loop , the officer makes a proposal and an employee approves it. Technology is accelerating, but the final responsibility remains clearly vested.
• In human on the loop , the officer performs tasks largely independently, while an employee supervises the whole. Control takes place at process or sample level.
• In the case of human out of the loop , the officer operates completely autonomously, which is only justified in low-risk tasks with clear frameworks.

This distinction is not a theoretical model, but a practical design choice. It determines how organizations think about governance, liability and risk management. As soon as AI acts, the responsibility does not automatically shift with it, it must be organized explicitly.

Practical example: from tens of minutes to one minute

Within Wortell, an agent has been developed for drawing up so-called non-standard change proposals within Managed Services. Previously, engineers spent between fifteen and forty-five minutes gathering context, analyzing previous changes, and developing a proposal.

The agent now asks additional questions, consults historical changes, weighs organizational context and generates a fully substantiated concept within one minute. The engineer checks and refines where necessary.

The result is not only time savings, up to tens of minutes per change, but also higher consistency and faster turnaround times for customers. Humans do not disappear from the process, but shift to control, assessment and optimization.

This is where agents show their true value: not as a replacement, but as a structural accelerator of knowledge-intensive processes.

The pitfalls of ill-considered deployment

Precisely because agents are powerful, there is risk in overestimation. A convincing demo is not yet a scalable solution. An agent who functions well today requires maintenance, monitoring and adjustment tomorrow. And human control alone does not guarantee safety.

Agents are reliable not because they are smart, but because they are well designed, tested and limited. Logging, audit trails, clear ownership and periodic evaluation are not optional additions, but preconditions.

Organizations that see agents primarily as cost savings miss the essentials. The true value arises when processes are consciously redesigned with the presence of a digital colleague as a starting point.

Three strategic choices

In the episode, this is summarized in three strategic choices that every organization has to make.

• Protect. Who is responsible for decisions an agent makes? Which frameworks apply? How is transparency guaranteed through logging and audit trails?
• Redesign. Will an agent be added to an existing process, or will the process be redesigned based on the idea that digital execution is a structural component? This is where the real competitive advantage arises.
• Automate. Which tasks have grown historically but add little value? Which controls can be smarter? Which actions are suitable for full autonomy?

These choices determine whether agents remain an incidental optimization or grow into a strategic tool.

The next step in AI maturity

The introduction of AI agents does not mark a technological upgrade, but an organizational maturity question. As with Copilot, the issue is shifting from availability to ability: does the organization have the design thinking, governance and change power to integrate agents sustainably?

Organizations that succeed in this do not distinguish themselves by the number of agents they deploy, but by the way in which they organize people and technology in a complementary way. They don't see agents as an experiment, but as part of their operational architecture.

In episode three of AI in business , that shift is made clear and concrete. Not as a vision of the future, but as a current development that is already visible in processes, service desks, finance departments and customer contact.

The question is therefore not whether agents will play a role, but where you start as an organization.