Description du poste
Acknowledgements: My thanks to Vanessa Huang for reviewing a draft of this blog and to Anne-Marie Ng and Sahil Menon for discussions on this topic.
The assumption that it’s necessary to have a lot of resources to do research on large-scale multi-agent systems is creeping into many of my conversations at the moment. It makes people worry they can’t work on big systems, and that large multi-agent safety is closed to them.
Though intuitive, this assumption is thankfully wrong. Extra resources are unhelpful for many problems in this area, and the scientist is really as pivotal as in single-agent safety. I’ll briefly explain how it’s possible to do research on big systems without running them, highlight some interesting and topical open problems, explain how smaller agent counts help control experiments, point out some approaches which don’t require any compute, and clarify the narrow domain of research which does need it.
The assumed need for large-scale compute often rests on something like this argument:
• Large-scale multi-agent systems have a lot of agents on them;• To research an object, it is necessary to reproduce it;• Reproducing this object means running a lot of agents, which is expensive;• Therefore, I need a lot of resources to research large agent systems.Premise Two is false. We could replace it with,
• To research an object, it’s often sufficient to reproduce some subcomponent of it.If the interesting subcomponent is a piece of system infrastructure, an agent, or a subgroup of agents, it might not be expensive at all. For example, here are some questions which we could answer for the cost of traditional single-agent evaluations:
• How do agents’ behaviour change when they interact with the system structures proposed in Infrastructure for AI Agents (Chan et al., 2025) and Virtual Agent Economies (Tomasev et al., 2025)?• What different roles can infrastructure fill in a large system, and how do these roles support or hinder the system’s design goals?• How