Speaker
Description
The LHCb collaboration at CERN spans from individual physics analyses to production computing. That breadth makes it a natural place for the agentic AI. The software stack grew over decades around human experts. The physics is split into internal working groups, which standard LLMs were not trained to distinguish. This HEP-specific workflow cannot be integrated with commercial tools without custom pipelines.
The agentic AI landscape also changes quickly. In an isolated environment with large amounts of internal knowledge, we cannot adopt new model or agent architectures on intuition alone: we need a scientific methodology to track impact, avoid regressions, and manage risks. Within LHCb we are therefore building dedicated tooling to give agents structured access to our data and services, and benchmarking them with metrics tailored to our workflows so that we can quantify how each change affects real tasks.
We are developing the LHCb Brain, an agentic chatbot whose router can dispatch requests across many internal services, much like branching pathways. At the same time, we are exploring IDE analysis assistance and fully encapsulated agents running inside the LHCb stack. What we have learned is that usefulness depends less on generic agent capabilities and more on overcoming fragmentation: connecting legacy services, domain conventions, and workflows that were never meant to be automated. This talk will give an overview of the current developments happening in LHCb.