Agentic AI

Agentic AI refers to artificial intelligence systems that operate with a certain degree of autonomy, make decisions, and pursue goals without constant human supervision. Agentic AI systems are based on foundation models with reasoning and acting capabilities and model-driven specialized AI agents that are combined to form multi-agent systems.

Agentic AI can be integrated into complex environments, where it can replace or facilitate existing workflows. In short, agentic AI creates added value by enabling companies to let AI independently take on real-world tasks, making processes more efficient and responsive.

The use of autonomous AI agents in professional environments requires reliability and control. Our research focuses on closing the gap between the latest research findings and the requirements of industry. We develop systems that can be seamlessly integrated into complex architectures while strictly adhering to data sovereignty and security standards.

• Specialized reasoning: How can we adapt domain-specific large language models to support agents in structured reasoning and decision-making in highly specialized fields such as law, medicine, and engineering?
• Edge & sovereignty: How can we develop agent systems that operate locally or at the edge of the cloud to ensure data privacy, reduce latency, and control inference costs?
• Reliable evaluation: How can we effectively benchmark the performance and safety of autonomous agents in dynamic real-world environments in accordance with practical requirements and European standards to ensure maximum benefit?
• Enterprise MLOps: What are the optimal architectural frameworks for deploying, monitoring, and maintaining agent systems in production? How can we optimize data quality for agent-based workflows?
• Extended capabilities: How can we expand the scope of agent-based AI by integrating simulation, operations research, and physical AI?