AI Isn’t Dumb—It’s Just Better Than Many Think (and AI Agents aren’t hype!)
I originally posted this article in German, but it seems that the phenomenon is not limited to Germany. Therefore, I would like to share my opinion in English as well. Inspired by the LinkedIn post from Stephen Klein: Agentic AI is 100% None Sense Designed To Scare You Into Spending Money on Consulting. I think here someone is mixing up Autonomous AI and AI Agents. But let‘s have a look on the initial post:
On a Saturday morning, while scrolling through the latest news on my phone, I came across an article on ZEIT ONLINE titled “KI-Agenten: Das können Sie selbst besser” by Eva Wolfangel. Additionally, I recall the extensive piece “Künstliche Intelligenz – Die große Sinnkrise” from August 9–11, 2024, by F. Holtermann, L. Holzki, and P. Alvares de Souza Soares in Handelsblatt.
Two major German media outlets—Handelsblatt and ZEIT ONLINE—portraying AI agents and generative AI as overhyped technologies. They speak of empty promises, lack of intelligence, and a potential bubble.
The central message from Handelsblatt: “In reality, they can only predict which word is most likely to follow in a sentence.” And from ZEIT ONLINE: “The term ‘AI agent’ is nothing more than a marketing gimmick.” – Eva Wolfangel. Also from the ZEIT ONLINE article: “The truth is: Everyone talks about AI agents, but no one knows what they actually are. Not even the big tech companies.”
As someone who implements this technology productively with clients and teams daily, I must say:
This is not just an oversimplification. It’s dangerously wrong!
What AI Agents Truly Are—and Already Are Today
Formally correct—but factually misleading. An LLM, combined with tools, retrieval, memory, and API access, becomes an agent—with measurable business impact. It’s neither all hype nor a miracle. AI agents are like distributed systems or a microservice architecture. Each agent takes on a specific task and solves it—automated and in real-time.
They can do things that an LLM (Large Language Model) or chatbots like OpenAI’s ChatGPT cannot:
- Retrieve data from systems like SAP or directly from databases
- Coordinate processes across multiple systems
- Make complex decisions
- Respond to context and work in teams—including with humans
…or generate speech, images, code, audio, video, or, for example, a 3D model
A Concrete Example from Practice
A real-world example—not from a pitch deck:
A customer calls support. An AI agent takes over the conversation, transcribes the speech, identifies two concerns: “My internet isn’t working” and “I want to cancel.” What happens next?
- The “Connectivity Agent” checks the line and the router
- The “Doc Agent” analyzes the appropriate manual and generates a step-by-step guide for resetting the specific router the customer uses
- The “Contract Agent” reviews cancellation rights, deadlines, and contract status—and offers a credit
- A human agent is only involved if the situation is complex or requires empathy
AI agents are a team of specialized experts—they work efficiently, transparently, and scalably.
Quote: “You Can Do It Better Yourself”
One might say, hey, any service employee could do that too!? Essentially, yes. But:
- Does every service employee have access to the necessary systems?
- Is the employee qualified to retrieve the information—i.e., do they master the systems?
- Or does the customer have to be passed from department to department, expert to expert, and wait on hold?
- Is the employee motivated enough to act?
Assuming an employee meets all these requirements: what happens in case of resignation, retirement, illness, …? The knowledge and experience are gone. The AI, however, remains.
Scalability, transparency, digitization—that’s what it’s about. And staying with people, it’s also about enabling individuals to do things they couldn’t due to their qualifications, thereby elevating employees to a common level.
What’s Really Missing: Understanding and Implementation
Many companies still believe AI is plug & play. That you turn it on—and suddenly processes are automated, decisions are made, and innovation arises. But that’s not how it works.
AI is a powerful tool—not magic.
Those without use cases, data, and change readiness will fail with or without AI. AI is here to stay; we should quickly harness this technology for our benefit. Artificial Intelligence (AI) is one of the fastest and most profound technological developments in human history.
- AI isn’t limited to a single field like earlier technologies but permeates many areas: medicine, education, art, law, programming, journalism, and more.
- AI models sometimes learn skills they weren’t explicitly taught—e.g., logical reasoning, translation, programming…
- This changes society—comparable to the invention of the printing press or the internet, but potentially even faster.
- Never before has a technology had the potential to change mass labor markets as well as creative processes, decision-making, and even human self-understanding.
Why Such Articles Are Dangerous
What ZEIT ONLINE and Handelsblatt overlook is the reality in practice:
- AI agents are already being used productively
- Companies are achieving measurable business impact
- AI is being integrated as a service layer in IT architectures
- All industries and functions like telecom, utilities, software development, HR, and marketing are benefiting concretely
The business cases. The process automation. The platforms already running today.
If decision-makers believe such articles, they halt innovation. They don’t invest. They lose their edge. And if we also unsettle people by suggesting that AI will steal their jobs and soon take over the world—well, then good night, Germany.
References
Wolfnagel, E. (2025). KI-Agenten: Das können Sie selbst besser. ZEIT ONLINE. https://www.zeit.de/digital/2025-03/ki-agenten-apple-openai-google-newsletter-kuenstliche-intelligenz
F. Holtermann, L. Holzki, P. Alvares de Souza Soares (2024). Künstiliche Intelligence – Die große Sinnkrise, 09.-11. August 2024, Wochenende. Handelsblatt.
https://live.handelsblatt.com/wp-content/uploads/2024/09/KI-Blase.pdf