Agentic AI Explained: What It Means for Your Business

Blog · AI agents

Agentic AI describes AI systems that pursue a given goal with little supervision: they plan the steps, use tools such as databases, business software or the web, and take action instead of just answering. A chatbot can describe what a quote might look like. An AI agent can gather the data it needs, prepare the quote in the right application and hand it to you for approval. That step from text to action is what makes agentic AI attractive for companies, and it is also why analysts urge caution.

In brief

Agentic AI pursues a goal, plans, uses tools and acts; generative AI mainly produces content.
According to Bitkom, 11 percent of surveyed German companies already use AI agents and 29 percent plan to.
Gartner expects more than 40 percent of agentic AI projects to be canceled by the end of 2027, mostly over cost, unclear value or weak risk controls.
The EU AI Act has no separate category for agents, they count as AI systems; a person should approve high-impact actions.

Published 25 Sep 2026Nikolai Schöbel and Jeremias Burger9 min read

Nikolai Schöbel and Jeremias Burger

Co-founders of Scalableloops GmbH. Nikolai Schöbel leads online marketing and AI strategy, Jeremias Burger the AI architecture. Both build AI systems and train teams on them in their own agency work.

On this page

What is agentic AI?
How is agentic AI different from generative AI and chatbots?
How far along are companies with agentic AI?
What do analysts expect from agentic AI in business?
What risks arise when AI acts on its own?
Why does human approval still matter for agentic AI?
What does the EU AI Act say about agentic AI?
How do companies put agentic AI to work?
Frequently asked questions
How to get started with agentic AI
Where the information on this page comes from

Definition

What is agentic AI?

Agentic AI is AI that accomplishes a goal with limited supervision. That is IBM's definition, and MIT Sloan describes it in similar terms as software that perceives, reasons and acts in digital environments to achieve goals on behalf of people. The individual programs are called AI agents.

Four abilities set it apart. Pursuing a goal: the agent receives an assignment rather than a single question, for example “prepare a reply to this customer enquiry”. Planning: it breaks the assignment into steps and decides on their order. Using tools: according to IBM, agents can search the web, call the interfaces of other software and query databases. Acting: the result is a completed piece of work, not a text someone still has to process.

IBM offers a simple illustration: a generative model recommends the best time to climb a mountain, while an agentic system also books the flights and hotels. For your business, agentic AI becomes relevant wherever people currently pull information from several systems and then act on it.

Distinction

How is agentic AI different from generative AI and chatbots?

Generative AI creates content, agentic AI completes tasks. MIT Sloan puts it this way: generative AI automates the creation of text, images and video, while AI agents go further, executing multi-step plans, using external tools and interacting with digital environments. Under the hood, many agents rely on a language model to understand and plan.

The line is blurry, and some vendors take advantage of that. Gartner calls it “agent washing”: existing assistants, chatbots or conventional automation rebranded as agentic AI without the capabilities to match. The overview below helps you tell the difference.

Feature	Chatbot and generative AI	Agentic AI
Trigger	A question or request in a chat	A goal reached over several steps
Output	Text, image or suggestion	A completed step, such as a created record or a draft prepared in the target application
Tools	Usually none, or web search only	Access to applications, databases and interfaces
Process	One answer per request	Plan, execute, check the result, choose the next step
Risk	Wrong or invented statements	Also wrong actions with an effect outside the company
Control	The person reads and decides	Approvals, permissions and limits must be defined up front

Adoption

How far along are companies with agentic AI?

Most companies are still at the beginning. For its press release of 14 September 2026, the German digital association Bitkom surveyed 603 companies with at least 20 employees. 57 percent use AI, up from 36 percent a year earlier. Among companies that use AI or are planning or discussing it, 11 percent already work with AI agents, 29 percent plan to and 31 percent are discussing it.

McKinsey paints a similar international picture. In “The State of AI in 2025”, 62 percent of respondents say their organisation is at least experimenting with AI agents, and 23 percent are already scaling agentic systems in at least one business function. There is still a wide gap between trying agents out and running them day to day.

For mid-sized businesses that is good news: if you start now with a clearly defined use case, you are not late. What matters is that your team understands what an agent does and where its limits are. That is exactly what our AI training covers.

11 %

of surveyed German companies that use or plan AI already deploy AI agents.

Bitkom, September 2026

Analysts

What do analysts expect from agentic AI in business?

Analysts see real potential alongside many false starts. Gartner predicts that by 2028 at least 15 percent of day-to-day work decisions will be made autonomously through agentic AI, up from zero in 2024. It also expects around 33 percent of enterprise software applications to include agentic AI by 2028, compared with less than one percent in 2024.

Gartner's release of 25 June 2025 also carries the sober counterpart: more than 40 percent of agentic AI projects will be canceled by the end of 2027. The reasons given are escalating costs, unclear business value and inadequate risk controls. Then there is agent washing: of the thousands of vendors promising agentic solutions, Gartner estimates only about 130 offer genuine agentic capabilities.

The takeaway for managing directors is practical. Success depends less on the technology than on choosing the right task. An agent pays off where a recurring process is clearly described, the benefit can be measured and mistakes surface early.

40 %

of agentic AI projects will be canceled by the end of 2027, according to Gartner's forecast.

Gartner, June 2025

Risks

What risks arise when AI acts on its own?

The biggest risk is not a wrong answer but a wrong action. MIT Sloan points to irregular reliability in important decisions, security gaps because agents access many datasets, and unclear accountability when something goes wrong. IBM notes that autonomy, the main benefit, can have serious consequences if an agentic system goes off the rails.

The security organisation OWASP lists this risk as “Excessive Agency” and names three causes. They work well as a checklist before you put an agent to work.

01
01
Too many functions
The agent can reach tools it does not need for its task. Every extra function is another way for things to go wrong.
02
02
Too many permissions
The tools run with more rights than necessary, for example an account that can delete data when the agent only needs to read it. The rule is the same as for employees: as much access as needed, as little as possible.
03
03
Too much autonomy
High-impact actions go ahead without human confirmation. OWASP's examples include a tool that publishes social media posts and an extension that can delete documents.

Approval

Why does human approval still matter for agentic AI?

Because a person can take responsibility and an agent cannot. OWASP explicitly recommends that a human approve high-impact actions before they are carried out. That covers anything with an external effect or that is hard to undo: an email to a customer, an order, a price change in your shop or a change to master data.

The principle is also written into law, although only for high-risk systems. Article 14 of the EU AI Act requires that such systems can be effectively overseen by people. Those responsible must be able to disregard, override or reverse an output and interrupt operation through a “stop” button or a similar procedure.

For everyday work in a mid-sized company, a simple split helps. The agent may research, sort, summarise and prepare drafts. Anything that leaves the company or moves money is approved by a person. That way you gain the time savings without giving up control.

Regulation

What does the EU AI Act say about agentic AI?

The AI Act does not treat AI agents as a category of their own. The European Commission's AI Act Service Desk states that the definitions of an AI system in Article 3(1) and of a general-purpose AI model in Article 3(63) are sufficient to cover agents. Article 3(1) describes an AI system as one that operates with varying levels of autonomy and generates outputs such as predictions, content, recommendations or decisions that can influence physical or virtual environments.

Which obligations apply depends on the use case. Since 2 August 2026, the transparency obligations of Article 50 apply to agents that interact with people or generate content. What this means for chatbots and customer conversations is covered in our article on AI disclosure under Article 50.

For high-risk uses, the Digital Omnibus Regulation (EU) 2026/1744, in force since 27 July 2026, has moved the deadlines: the rules for high-risk systems under Annex III apply from 2 December 2027, those under Annex I from 2 August 2028. From then on, high-risk agents must also meet the additional requirements of Chapter III.

Article 4 on AI literacy has applied since 2 February 2025. In the version amended by the Omnibus, providers and deployers are to support the development of their staff's AI literacy. Our article on the AI literacy obligation under Article 4 explains what that means in practice. This article is not legal advice.

Practice

How do companies put agentic AI to work?

Best with an agent that knows your business and works inside your existing processes. Most tasks where agentic AI helps are unspectacular: pre-sorting enquiries, pulling together information from several applications, preparing drafts of replies, quotes or reports. The benefit comes from taking this work off your specialists while leaving the decision with them.

Our AI agent system shows what that looks like day to day. It runs on your own server, is not tied to a single AI provider, knows your business and works with the software you already use. Anything that goes out is approved by a person on your team.

Before an agent can act, your business also needs to be found and understood, including by other AI systems. How to become visible in AI answers is covered in our article on AI SEO.

Frequently asked questions

What is the difference between agentic AI and AI agents?

Agentic AI describes the approach: AI that pursues goals, plans, uses tools and acts. AI agents are the individual programs that do this. In practice the two terms are often used interchangeably.

Is ChatGPT agentic AI?

A plain chat that answers a question with text is generative AI. A system becomes agentic when it pursues a goal over several steps, uses tools such as applications or databases and carries out actions. Many vendors are adding such features to their products, so it is worth checking what a tool actually does.

What are examples of agentic AI?

IBM describes an agent that works out the best time to climb a mountain and then books flights and hotels. In a business, typical tasks include pre-sorting enquiries, gathering information from several applications and preparing drafts that a person approves.

Is agentic AI allowed under the EU AI Act?

Yes. The AI Act treats AI agents as AI systems, so the same rules apply. Which obligations kick in depends on the purpose: transparency obligations under Article 50 since 2 August 2026, and high-risk requirements under Annex III from 2 December 2027.

Why do so many agentic AI projects fail?

Gartner names escalating costs, unclear business value and inadequate risk controls as the main reasons and expects more than 40 percent of projects to be canceled by the end of 2027. A clearly scoped use case with measurable value lowers that risk.

Does agentic AI need human approval?

For high-impact actions, the security organisation OWASP explicitly recommends approval by a person. For high-risk systems, Article 14 of the EU AI Act requires that people can effectively oversee them and stop them if needed.

What now

How to get started with agentic AI

01
Pick a task
Look for a recurring process in which staff gather information from several applications and then produce a draft.
02
Set the limits
Decide which applications the agent may use, with which permissions, and which steps a person approves.
03
Enable your team
Make sure the people involved understand how the agent works and where it can make mistakes. This also supports AI literacy under Article 4.
04
Measure the benefit
Define up front how you will recognise success, such as time saved per case or faster replies, and review it after a few weeks.

Agentic AI shifts the question from “What can the AI write?” to “What may the AI do?”. Draw that line deliberately and you gain time without handing over responsibility.