The sheer volume of data and complexity of modern scientific challenges necessitate tools that go beyond mere analysis. The vision of an "AI Co-scientist" – a true collaborative partner in the lab – requires sophisticated engineering to bridge the gap between powerful AI reasoning and the dynamic reality of physical experiments. This talk dives into the engineering required to build robust AI Co-scientists for hands-on research. We will explore scalable architectures, such as multi-agent systems leveraging foundation models like Gemini for complex reasoning, hypothesis refinement (inspired by the "generate, debate, evolve" paradigm described in recent AI Co-scientist research), and intelligent tool use. The core focus will be on the engineering challenges and solutions for integrating diverse, real-time empirical data streams – visual data from cameras, quantitative readings from sensors, positional feedback from actuators, and instrument outputs – directly into the AI's reasoning loop. I will illustrate this with concrete, technically detailed examples in chemistry (adaptive reaction monitoring), robotics (vision-guided assembly with SO Arm 100 and LeRobot library), and synthetic biology (real-time bacterial growth monitoring & interpretation). We'll discuss engineering strategies for handling data heterogeneity, latency, noise, and enabling the AI to interpret, correlate, and act upon live experimental feedback. Finally, we will touch upon how thoughtful engineering of these AI Co-scientists can contribute to democratizing access to advanced scientific capabilities.