From scientific copilots to closed-loop labs: building, benchmarking, and deploying agentic AI systems for the next generation of life-science discovery.
The life sciences are entering an era of agentic AI — systems built on tool-using and reasoning frameworks 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 LLMs and by a rapidly growing stack of biology-specialized foundation models for proteins, genomes, and single cells.
Yet the field remains strikingly young. There is still little consensus on how to build effective life-science agents, when biology-specialized models are necessary versus when general-purpose LLMs suffice, and how to deploy such agents toward the ultimate goal: accelerating biological discovery and drug development. AgenticLS brings together researchers from machine learning, computational biology, experimental biology, drug discovery, and lab automation to tackle these questions as a building, deployment, and evaluation problem.
How should we design agent harnesses, orchestrate multi-agent systems, manage long-horizon memory and context, and integrate biological tool ecosystems? What metrics and rewards guide agents toward novel discoveries?
When are frontier general-purpose models (e.g., GPT, Claude) sufficient, and when do biology-specialized models (e.g., AlphaFold, ESM) materially improve planning, reasoning, or downstream outcomes — and can specialists improve via recursive self-improvement?
How should agentic AI autonomously interface with robotics, assay platforms, and human scientists to enable reliable, iterative experimentation in closed-loop laboratories?
We welcome submissions across two tracks spanning the full agentic life-science stack.