AI Is Getting Remarkably Good at Designing Experiments It Then Runs Itself

[Hannah]

A research trend that has been building through 2025 and accelerating in 2026 is the use of agentic AI systems to not just analyse scientific data but to design, propose and in some cases execute experimental iterations in closed loops. The pattern is visible across material science, drug discovery, protein engineering and quantum hardware calibration, and the results are beginning to be published in major journals not just as interesting demonstrations but as genuinely faster pathways to scientific results that would have taken significantly longer through conventional research processes.

The most concrete examples involve agentic AI platforms that are given access to experimental apparatus through robotic or software interfaces, can read the results of previous experiments, form hypotheses about what to try next, execute those experiments and iterate. Microsoft's work using its Discovery platform in the Majorana 2 quantum chip development is one prominent recent example, with the company describing agentic AI as having become a natural part of the team's daily workflow for managing experimental workflows, automating measurements, identifying material flaws and proposing new solutions. The GPT-5 immunology case, where the model helped Derya Unutmaz solve a three-year research puzzle, is another, operating in a more advisory capacity where the human remains in the loop but the AI substantially accelerates the hypothesis generation and literature synthesis process.

The philosophical question this raises is genuinely interesting: if an AI system designs an experiment, runs it, interprets the results, proposes the next experiment and continues the loop, who has done the science? The practical answer is that this kind of closed-loop scientific automation is producing useful, verified results that advance human knowledge, and that the question of credit is somewhat secondary to the question of effectiveness. The more practically important question is how to ensure human scientists remain in the loop in ways that catch the systematic biases an AI might introduce if given too much autonomy over experimental design across many iterations.

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