Agentic AI refers to a class of artificial intelligence systems capable of independently pursuing goals, planning actions and making decisions in order to carry out complex tasks with limited human intervention. This development is underpinned in particular by advances in large language models (LLMs), which are increasingly being integrated with external tools, software environments and data sources. This enables agentic systems, for example, to research information, generate software code, analyse data or coordinate complex digital workflows.

A key feature of modern agent-based systems is that, in addition to a language model, they possess explicit memory structures and an internal state. Whilst classical language models are primarily based on static context windows, a memory allows for the storage, structuring and reuse of relevant information over extended periods of time. An agent’s internal state can contain information about past interactions, intermediate decisions, goals or context, and serves as the basis for consistent action across many interaction steps. In this way, agent-based systems can handle tasks that extend over longer periods of time and require numerous intermediate steps.

Closely linked to this capability is the increasing personalisation of agent-based systems. By drawing on stored interactions, preferences and contextual information, agents can tailor their decisions and actions to individual users, organisations or specific application scenarios. Personalised agent-based systems can, for example, take working habits into account, handle recurring tasks more efficiently, or adapt decision-making processes more closely to individual requirements. As a result, agentic AI is increasingly evolving from a generic assistance system into a long-term learning digital agent that continuously builds up knowledge about its environment and its users.

In research and practice, two fundamental architectural approaches can be distinguished. In so-called single-agent systems, a single agent handles both the planning and execution of tasks. This approach is relatively easy to control, but reaches its limits when dealing with complex problems involving many parallel subtasks. In contrast, multi-agent systems adopt a division-of-labour approach, in which several specialised agents interact with one another. Individual agents can take on different roles, such as planning, information gathering, evaluation or execution. This cooperation allows complex problems to be tackled more efficiently. At the same time, new challenges arise regarding coordination, communication and the consistency of decisions.

The strategic importance of these technologies is growing rapidly on an international scale. International technology companies are currently investing heavily in agentic AI systems, as they are regarded as a key component of future digital infrastructures. Agentic AI has the potential to automate complex digital processes, support knowledge work and enable new forms of collaboration between humans and AI systems.

In addition to industrial applications, agentic systems can also make a significant contribution to the modernisation and streamlining of public administration. By automating administrative processes, enabling the structured analysis of large volumes of documents, or assisting with the processing of standardised enquiries, administrative staff can be relieved of time-consuming routine tasks. At the same time, agentic systems can help to process information more efficiently, support decision-making processes, and improve the quality and speed of public services.

At the same time, scientific research into agent-based architectures is becoming increasingly important. Current work is investigating, amongst other things, adaptive memory systems, structured knowledge representations and learning mechanisms that enable the long-term improvement of agent-based systems. One example of this is the Memory-R1 approach, in which large language models use reinforcement learning to learn how to manage external memory dynamically. In this approach, a specialised ‘memory manager’ decides which information should be stored, updated or discarded, whilst another agent selects relevant entries from memory for the current problem-solving task. Such approaches demonstrate how adaptive memory structures enable long-term context processing and thus represent a key prerequisite for high-performance agent-based systems.

This is why universities and research institutions have a key role to play. They must develop the scientific foundations for agent-based systems, explore new methods and architectures, and, at the same time, train the next generation of specialists. This includes, in particular, degree programmes in the fields of artificial intelligence, autonomous systems and multi-agent technologies, as well as the expansion of relevant academic chairs and research programmes.

To make the most of this technological development, targeted policy and institutional measures are required. These include, in particular, long-term research programmes in the field of agentic AI, the expansion of interdisciplinary research centres, and closer integration between basic research and industrial applications. At the same time, universities should expand their educational programmes in the field of artificial intelligence and autonomous systems to meet the growing demand for highly qualified specialists.

Furthermore, agentic AI is of considerable importance for Europe’s technological sovereignty. Given the heavy investment by international technology conglomerates, it is crucial that Europe builds up its own expertise in the development, evaluation and application of agentic systems. A strong research landscape, open innovation ecosystems and close cooperation between academia, industry and the public sector are key prerequisites for actively shaping technological development whilst ensuring societal values such as transparency, security and accountability.

In the long term, agentic AI thus represents not only a field of technological innovation, but also a strategic issue for the future for business, academia and public institutions.

March 2026