Agentic AI for Biological Discovery: Toward Closed-Loop Life-Science Intelligence

The life sciences are entering an era of agentic AI, systems that go beyond static prediction to read literature, call specialized tools, plan multi-step analyses, propose experiments, and in some cases interact directly with laboratories and robotics. This shift is enabled both by frontier large language models and by a rapidly maturing stack of biology-specialized foundation models for protein structure, protein design, genomes, and single cells. Yet the field is strikingly young: there is little consensus on how to build an effective life-science agent (harness design, multi-agent orchestration, memory, tool ecosystems), how to deploy and evaluate one in high-stakes settings such as drug discovery and medicine, or when biology-specialized models are needed versus when general-purpose LLMs already suffice. This workshop brings together researchers from machine learning, computational biology, experimental biology, drug discovery, and lab automation around four open questions: (i) generalist vs.\ specialist agents; (ii) systems-design for building and deploying agents; (iii) lab-in-the-loop integration with automation, robotics, and human scientists; and (iv) evaluation, reliability, and safety of scientific agents. We solicit contributions across two tracks: Building Agentic Systems for Life Science (architectures, harness design, literature agents, alignment, RL) and Closed-Loop Discovery and Applications (autonomous labs, biological design, single-cell and multi-omics agents, benchmarks, biosecurity).