Scalable causal discovery and reasoning for Agentic AI

Develop scalable frameworks for causal discovery and causal reasoning to provide causal foundations for LLM-based multi-agent Agentic AI systems, enabling robust generalization, safe coordination, and counterfactual or interventional planning under distributional shift.

Background

In surveying foundational gaps for Agentic AI, the paper argues that current systems lack causal foundations, limiting their robustness and capacity to generalize in dynamic environments. This deficiency affects coordination, planning, and error recovery across interacting agents.

The authors explicitly identify scalable causal discovery and reasoning as unsolved challenges, linking them to the need for principled design and reliable multi-agent orchestration beyond ad hoc implementations.