Autonomous agents are changing how we conduct science
AI-native research hands the experiment loop to agents and changes the researcher’s role.
We turn emerging ideas into things people can actually try, then use what we learn to help decide what should be built, changed, or stopped.
AI-native research hands the experiment loop to agents and changes the researcher’s role.
How our team shares workspaces and context with the agents we work with.
IntroductionsWhy we exist, how we work, what we're building, the questions we're still exploring — and what you can expect to read here.
InsightSovereign AI, and why getting it means running your own platform. Scope to be defined by the author.
InsightMost team inefficiency is not people working slowly. It is people working on the wrong thing, working from missing or wrong context, or working inside a process that quietly wastes their time. All three are visible in the artefacts a team already produces — issues, epics, plans, the trail of how work actually moved — and all three are exactly the kind of pattern an agent will find because it will read every one of them and a person will not. The worked example is the team's own work on issues, epics and planning: what tightening those up actually changed, and what the agent noticed that nobody had written down. The wider argument is that operational discipline normally decays because it depends on someone having the attention to enforce it on a busy week. An agent doesn't have busy weeks. It can keep checking that an epic still has a stated outcome, that an issue carries the context needed to start it, that the plan and the board have not silently diverged — and keep steering the team back rather than filing a report nobody reads. Worth taking positions on: which signals are real and which are process theatre; that the point is to surface the problem to the team, not to score people; and where this stops — an agent can say the context is missing, but it cannot decide what the team should care about this quarter.
DAM, our open source platform for running long-lived AI agents — each in an isolated sandbox behind a zero-trust credential gateway, driven from the browser, the CLI, Slack, or on schedules. We're still exploring where the line falls between what the agent handles and what the platform should.
The serving substrate agentic research runs on, tuned for the throughput and cost of long-running autonomous work — so experiments are bounded by ideas, not by capacity.
Interviews, studies, flow boards, coded prototypes — whatever makes an idea concrete enough to put in front of researchers and challenge before it's real. What breaks tells us what to build, change, or stop.