Infrastructure for Science that Compounds in the Age of AI Agents

AI agents that autonomously conduct research risk breaking a delicate balance in science. The scientific paper is a lossy format: methodological choices and assumptions compressed into a few pages, with peer review and community scrutiny providing the warrant that results are sound. This trade-off worked because the rate of new work stayed roughly matched to the community’s capacity to scrutinize it. Agents now threaten that balance, accelerating production far faster than scrutiny can keep up and deepening an already well-documented trust and reproducibility crisis. But the same technology is also an opportunity: agents can document exactly how results are produced at a level of detail that was previously too onerous to maintain by hand, if we build the infrastructure to capture it.

Closing that gap is what we’re building toward at Lightcone Research, a new initiative between UC Berkeley and the French National Centre for Scientific Research (CNRS). We are building a new standard ensuring that conforming analyses are not only reproducible by default, but also inspectable, verifiable, and extendable - every result traceable to the assumptions that produced it, decisions substitutable without rewriting entire pipelines, and analyses able to cite and build upon one another. Done right, this is what allows AI-powered science to remain trustworthy and continue to compound. Because infrastructure of this kind has to be built with and for the community that relies on it, our project is open-source, collaborative, and designed for shared stewardship from the start.

In this talk, I will survey the current state of agentic AI in science and the pitfalls it is exposing, introduce the Lightcone Research project and its agentic research stack, as well as share lessons from working with UC Berkeley early testers and reproducing flagship cosmology results.