Die Autonomie-Illusion: Warum KI Agenten oft nicht autonom sind und was das für Unternehmen bedeutet

The illusion of autonomy: Why AI agents are often not autonomous and what that means for companies

Have you ever seen a real AI agent in the wild?

I mean a system that genuinely plans multiple steps on its own, uses tools independently, makes decisions, and does all of this without waiting for a human at every critical juncture.

Honestly, I have rarely seen one.

And yet agentic AI is everywhere right now. Conferences, strategy papers, product announcements, and countless social media posts all carry the same narrative: the breakthrough is happening now, processes are becoming truly autonomous.

The reality looks considerably more sober.

According to current McKinsey data, only 23 percent of surveyed organizations have scaled an agentic system anywhere in their business. Another 39 percent are experimenting with it. Interest is high, but the step from pilot to broad adoption remains difficult. Other surveys, including one from LangChain, paint a more optimistic picture of productive deployments. That is precisely why it is worth drawing clean distinctions between pilot, production, and genuine scale.

The real misunderstanding

Many solutions sold today as agents are, in practice, more like tightly guided workflows with language model support than genuinely autonomous systems. Gartner is already warning about what it calls "agent washing," where existing assistants, chatbots, or automation tools simply get relabeled.

That is not automatically a bad thing. For many organizations, this kind of controlled entry point makes far more sense than chasing the narrative of full autonomy.

So the interesting question is not whether models can produce impressive demos today. The more interesting question is whether organizations can embed these systems into real operations safely, transparently, and economically.

Why the bottleneck is rarely the model

A recent MIT analysis of a healthcare study is particularly instructive. Less than 20 percent of the effort went into prompting and model development. More than 80 percent went into sociotechnical implementation, meaning data integration, processes, governance, validation, and monitoring.

That does not make the technology unimportant. But it does mean the bottleneck is usually not the model itself. It lies in embedding the system into the organization.

Agentic AI is therefore not just a topic for data science or IT. It is a topic for operating models, accountability structures, risk management, and control architecture.

Once a system acts, responsibility becomes real

As long as a model only responds, the risk stays limited. But the moment a system modifies records, sends messages, initiates bookings, or accesses operational data, the situation changes fundamentally.

At that point it is no longer just about the quality of an answer. It is about accountability for actions.

The IMDA framework from Singapore addresses exactly this. Organizations should assign responsibilities clearly, define critical checkpoints, build in technical safeguards, and design human oversight in a way that remains effective even as systems become more capable. This matters especially because growing reliability tends to increase the risk that people extend trust too quickly. NIST describes this as automation bias.

A note of legal caution: the deploying organization typically retains responsibility for approvals, governance, and control. But actual liability can be complex depending on jurisdiction, contracts, product design, and the interplay between multiple providers.

Why many initiatives stall despite working technology

Gartner projects that more than 40 percent of agentic AI projects could be discontinued by the end of 2027, citing rising costs, unclear business value, and insufficient risk controls.

That is notable because it inverts the usual narrative. The central problem is not model failure. It is the difficulty of setting these systems up cleanly from an operational standpoint.

Recurring issues include poor traceability, unclear roles, weak control mechanisms, difficult integration with legacy systems, and inadequate monitoring in production. This is exactly where it gets decided whether a demo becomes a reliable business process.

What is shifting right now

In January 2026, Singapore's Infocomm Media Development Authority published a state-backed Model AI Governance Framework for agentic AI. Several accompanying sources place its presentation at the World Economic Forum in Davos on January 22, 2026. The document is among the clearest public guidance available so far for the responsible deployment of such systems.

The World Economic Forum is also working on more concrete foundations for evaluation and governance. The message is similar: when agents move out of experimentation and into real processes, organizations need clear roles, layered safeguards, ongoing monitoring, and robust assessment procedures.

What decision-makers should take away

First: governance is not a brake on progress. It is the precondition for agentic AI moving beyond pilot status at all.

Second: a tightly guided workflow with language model support is not automatically the inferior solution. In many cases it is the more sensible choice today, because it is easier to control and can deliver real value faster. This is an interpretive position, but it aligns well with current findings from Gartner, McKinsey, and MIT.

Third: the most important leadership question is no longer just what a system can do. It is what the system is permitted to do, how it is monitored, and who carries responsibility when something goes wrong.

My take

The autonomy illusion is one of the biggest misunderstandings in the current conversation about agents.

Not because far-reaching automation is impossible. But because it requires organizational maturity that many companies have yet to build.

The productive path forward runs not through bold promises, but through clearly bounded use cases, clean role definitions, technical safeguards, effective oversight, and trust built incrementally.

Understanding that does not make you slower. It means you are building on solid ground.

References

Back to blog