What we learn running lab science with agents: protocols, plate data, agent design, and how we work. Founder essays live at Building Elnora; company announcements at News.
Protein structure prediction, image analysis and AI-written research code are already real. A lab that wants to automate lab data analysis with an AI agent needs one that reads the files its own instruments write.
A scientist's job is a bundle of tasks that are changing at different speeds, and the labs that adopt now, while still teaching the underlying skill, are the ones that keep both the speed and the judgement.
Vendors who want a product named in a protocol have to give Elnora the same evidence a scientist would check: datasheets, application notes and the runs behind them, including the ones that failed.
How do scientists use AI agents in the lab? Increasingly to run computational biology, quality control and design of experiments work outside their formal training, as long as they still supply the judgement that decides whether the answer is right.
An answer key only covers the questions that have one. How to evaluate an AI agent for scientific work when two good scientists would disagree, by scoring provenance, negatives, and the judge itself.
Every list of the best AI tools for biology research is written from demos. The method we run on Elnora: synthetic data from a model you control, a withheld answer key, and defects you never mention.
Scientists open the CSV their machine wrote and prepare it with colour codes and tabs. Handing that to an AI tool to analyze plate reader data passes on the answer you already expected.
The loop behind every document at Elnora, a shared knowledge base, an agent that drafts from it, a review pass and the result filed back, is how Elnora works as an AI agent that drafts lab reports from your data.
