GALTS

Contact person: Tobias Mahler

Status: Project

Agentic AI is evolving quickly, and definitions remain contested. A workable characterisation is that such systems break high-level goals into sub-tasks, plan, retain memory across tasks, invoke external tools and services, and coordinate with other agents. This marks a shift from static, single-task models towards autonomous actors that intervene in the world, potentially with minimal human oversight. The shift promises significant gains in productivity, but it implies risks and strains existing governance frameworks. GALTS brings together law, computer science, philosophy, social science and risk governance in four prongs: (1) Design for safety, accountability and compliance: how agentic AI systems can be technically designed to ensure safety and to support traceability and legal compliance. (2) Systemic risk management: how organisations can anticipate, assess and manage systemic risks from agentic AI, which often stem from feedback loops between multiple agents, their environments, and wider socio-technical systems. (3) Regulatory challenges: whether current frameworks, including the EU AI Act and the GDPR, are adequate to the risks that agentic AI poses. (4) Human and AI agency: goal formation in human-AI interaction, empirically and normatively, including how agentic systems shape immediate preferences and longer-term interests, whose objectives they pursue, and what standing machine-generated objectives can claim in collective decisions.