locally sounds straightforward. Download the weights, start the server, and send requests. That works for a chatbot, but it doesn’t automatically work for an agent. In my case, I’ve been building an agent for automated single-cell RNA-seq analysis. The idea is that, given raw data, the agent can run the full pipeline on its own, deciding which tools to call, reading the results, and working through the analysis step by step. You might ask why not just use something like Claude Code with a single-cell analysis Skill. The short answer is that for scientific workflows, that’s not quite enough. Skills are ultimately prompts and can thus be overridden or ignored. More importantly, scientific work requires reproducibility and provenance tracking: knowing exactly which parameters were used, which cells were filtered, which clustering resolution produced which result, etc. That record needs to be structured and persistent, not reconstructed from a conversation.…