Excited to see X-Scientist launch! Proud to be one of the core builders of its harness alongside an incredible team @Xaira_Thera. Here’s what building it taught me about domain-specific agents 👇 1, The model is general. The harness is where domain knowledge lives. Frontier models already “know” a lot of biology. What they lack is the scaffolding to think like a drug hunter: which evidence counts, what order to reason in, when to change course. Our key design choice was to separate scientific thinking from execution. Once coordination and verification are their own layer, they become something you can optimize, and that’s where domain knowledge gets encoded. 2, Tools > prompts. A drug discovery agent needs tools built for its domain. We built ours around our own models like X-Cell and X-Design, alongside bioinformatics and scientific analysis tools. Each tool has to return outputs the model can reason over, not raw dumps. 3, Your verifier is your moat. Coding has unit tests. Biology has noisy data and slow wet-lab loops. Deciding what “good” looks like at each step, and checking it automatically, is what keeps an agent grounded instead of drifting into confident nonsense. It’s also what lets scientists trust its output and step in where it matters. 4, The thinking layer learns. Because scientific reasoning is separated from execution, it can improve on its own. Every run leaves a trace of which strategies produced real evidence and which burned effort. We feed that back into how the agent plans, allocates and verifies. Each run makes the next one a better scientist. General-purpose agents will keep getting better. But in high-stakes science, the last mile belongs to whoever builds the best domain harness. 🚀