Open Source Summit Korea 2026

As AI agents become increasingly capable of generating code and executing complex workflows, their use in research is still limited by concerns about rigour and reproducibility. This session introduces ado, an open-source framework that brings structure to agent-driven scientific experimentation.

ado defines schemas for the core elements of a discovery process: the problem space and how to explore it. Agents iteratively propose and refine experimental campaigns as validated configurations based on these schemas, while ado handles execution. This separation of research intent from execution constrains agents to focus on the research task, reducing hallucinations and the need to write boilerplate code. Combined with a set of agent skills for formulating problems, creating and running experiments, and analysing their results, ado provides a framework for end-to-end agent-driven discovery workflows.

Whether you are an experienced researcher or new to computational experimentation, this talk presents a practical model for integrating AI agents into research workflows while keeping experimentation structured, transparent, and reproducible.