What “Agentic AI” 🤖 is and why waiting is no longer an option ⚠️

Multi-agent systems: When agents are organized into teams

The most technologically advanced form of agentic AI is the so-called multi-agent system, an architecture in which several specialized AI agents work together in a coordinated and collaborative manner. In such an architecture, for example, one agent might handle data acquisition from internal and external sources, a second might assess risks and develop courses of action, a third might produce the final documents or initiate automated process steps, while a central coordination agent monitors the overall process and aggregates decisions. The result is no longer rigid, linear automation, but rather an autonomous digital process organization that adapts to changing conditions.

McKinsey’s 2025 global AI survey observes a significant shift from mere experimentation to the deep integration of autonomous systems into core production processes, with the explicit goal of structurally increasing resilience and efficiency. A survey of over a thousand business leaders conducted by the Capgemini Research Institute found that over 80 percent plan to integrate agentic AI into their core processes within the next three years. Nearly two-thirds of these executives expect autonomous agents to significantly improve customer service and customer satisfaction.

Related to this:

The blind spot: When efficiency promises meet reality

Despite these impressive growth curves, there is a downside that is often overlooked in the enthusiasm. The IBM CEO Study 2025 soberingly reveals that only 25 percent of Agentic AI projects have achieved their initial financial targets, and a mere 16 percent have been successfully scaled company-wide. IBM itself executed a remarkable strategic U-turn in mid-March 2026: The company, which had previously aimed to replace thousands of jobs with AI, announced it would triple its recruitment of entry-level employees because the anticipated efficiency gains had been largely negated by high technology costs and implementation expenses.

The reality of costs is more sobering than the marketing promises. In Germany, an AI agent pilot project with true ERP and CRM integration will cost between €30,000 and €80,000 in 2026, while a company-wide rollout will cost between €90,000 and €200,000. Over three years, the total cost of ownership amounts to one and a half to three times the initial investment when platform costs, maintenance, updates, and continuous development are factored in. Gartner also warns that by 2027, around 40 percent of all Agentic AI projects could be abandoned if sufficient risk controls and governance structures are not implemented.

What governance means when AI makes decisions independently

Perhaps the most important question arising from the proliferation of agent-based systems is not technological, but organizational. If an AI agent independently places orders, initiates contracts, sets prices, or allocates resources, who bears the responsibility for the consequences? Who monitors whether the objective assigned to the agent still aligns with the company’s interests? Who prevents an agent in a multi-agent system from triggering a cascade of errors, where an incorrectly interpreted data signal is propagated through all downstream processes?

These questions are not rhetorical. They represent the real challenge of implementing agentic AI. Establishing a so-called “human-in-the-loop” principle, which mandates human involvement at defined decision points, is now considered a fundamental prerequisite for any responsible use of autonomous systems. Leading providers and research institutions emphasize that monitoring, compliance structures, and clear lines of responsibility must not be secondary considerations but rather embedded in the architecture of an agentic system from the outset. Those who neglect this dimension risk not only malfunctions but also legal consequences under the EU AI regulations, which will fully come into force in August 2026.

The strategic imperative: Why waiting is no longer an option

Many medium-sized companies are still observing the development of agent-based systems from a safe distance, overwhelmed by the complexity, costs, and compliance requirements. This reluctance is understandable, but strategically risky. The decisive competitive advantage doesn’t arise from simply introducing AI agents, but from systematically identifying those processes where autonomy actually makes a measurable difference. A rule of thumb from practice is: if a process requires more than ten hours of manual effort per week and is structured enough to be described using rules, then an AI agent is almost always economically justified.

German market leaders like Siemens, SAP, and Deutsche Telekom have long since made this calculation and are investing heavily in autonomous systems. For smaller companies, a realistic entry point today lies in simple, well-defined processes, such as email triage, automated reporting, or supplier communication, with investments starting at two to five thousand euros for a first, functional agent. The crucial insight is not which technology is chosen, but whether the time saved can be translated into genuine business performance. An agent that handles ninety percent of a support task will pay for itself within one to three months compared to a full-time employee.

The direction is clear: autonomy is becoming the norm

Agentic AI is not the final stage of technological evolution; it is the beginning of a new phase. The development from multi-agent systems to hierarchically organized, mutually controlling, and learning agent networks will fundamentally redefine the possibilities of what is achievable with software over the next three to five years. Processes that currently require human decision-making will gradually become autonomous—starting where the data is clear, the rules are established, and errors are tolerable.

In its strategic roadmap for 2026 and beyond, SAP has announced plans to integrate Agentic AI directly into all core business processes, from integrated enterprise planning and digital manufacturing to logistics execution. The goal is a world where planning is more predictive and execution is largely automated. What is considered an ambitious pilot project today will be the minimum standard that customers, partners, and capital markets expect from modern companies in three years. The strategic question is no longer whether to start with Agentic AI, but how quickly one can build a viable, well-managed, and scalable architecture from experimentation.