Researchers have introduced RegNetAgents, a multi-agent framework for identifying regulatory driver candidates in cancer genomics. The system is designed to work across heterogeneous gene regulatory networks through structured queries.
The paper is relevant because biomedical AI often requires coordinating evidence from many sources rather than producing a single free-form answer. Multi-agent workflows may help break complex scientific analysis into specialized steps.
The work is still research-stage, but it shows how agentic AI methods are being adapted for domain-specific scientific discovery rather than only general productivity tasks.