Mission¶
Vision¶
Science that uses agentic AI well: agents do the connecting, scientists keep the thinking. Agentic AI is most valuable in science when it links data, tools, and models across groups and institutions, while the epistemic core of research (judgement, provenance, accountability) stays with humans.
Mission¶
Maintain the shared, citable, current record of best practices for applying agentic AI to science. Guidance written as static reports is outdated before it is published; this record is revised at the pace of the field, versioned for citation, and open to challenge. It aggregates what task forces and institutions learn, so organisations build on each other's experience instead of writing siloed reports.
Scope¶
Current developments around generative AI, particularly large language models, impact fundamentally how science is done. This living document aims to capture anything that matters in this frame. The test for any practice, document hook, or discussion:
Would adopting this change how science is planned, performed, evaluated, communicated, or governed?
In scope: research workflows and methods; scientific services, data resources, and infrastructure; provenance, citation, and evaluation of agentic systems; research skills and training; governance of AI within scientific institutions.
Out of scope: national economic and industrial policy, energy and compute geopolitics, security and defence, international treaties. When such material directly constrains scientific practice, the record cites it as context; it does not distill or debate it.
The record also draws a method boundary. It covers the practice of applying agents to scientific work. The design and validation of AI methods as scientific instruments (predictors, generative models, classifiers) have their own established community norms (FAIR, DOME, model cards, datasheets, REFORMS). The record cites these rather than restating them, and treats an agent or model as a method chosen for a task, not a default (see Match the method to the task).
The same test governs the library: a distillation of a broad policy report keeps the passages that fit into this scope.
Audiences¶
The record is written for three roles. There are overlaps, and most practices speak to more than one; practice pages carry audience-specific section for each.
- Practitioners: scientists and research groups using agentic AI in their daily work.
- Providers: the people who build and operate scientific services, data resources, and tools that agents use (for example the teams behind research-infrastructure services).
- Governance: scientific management, from institute leadership to head offices and funders, deciding what to enable, require, and resource.
How the record is made¶
Two groups shape adoption.
Pioneers adopt early and learn by doing; they exist in every audience but concentrate among practitioners. The best support for them is to remove obstacles and observe. Pioneers write these practices or ignore them; either is fine.
Settlers are far more numerous. They come after the pioneers and build things meant to last, so they need reliable, current guidance. The practices are learned from the first group and written for the second, across all three audiences.
How it stays current¶
The record is maintained on GitHub. Changes go through public review. A dated release is cut monthly and receives a DOI, so the record can be cited by scientists and by institutional strategy documents alike. Every practice records which organisations endorse it, and any organisation can propose, challenge, or endorse practices; the value of the shared record grows with every organisation that joins. See Partners, Governance, and Releases.