Shagility treats LLMs like team members by assigning them personas and policies, mirroring how he manages humans. In hospitals and high-stakes work, AI should only assist or answer questions, never act alone. Large organizations impose constraints that limit AI experimentation, while independent teams have more freedom to try new approaches.
Chapters
systems thinking. Like you said, I can change this little bit here, but actually has no impact because of all these other things. I think we have to understand how humans work. What I'm finding is in my harnesses, I'm more and more treating the LLMs like agents or employees that like humans, and I'm giving them personas and policies and ways of working because when I lead teams, those things worked, right? We have to-dos and doings and dones, and we keep track of them because As a team, it was easy to see what people were working on quickly by doing that. I'm getting the LLMs or the GenAI tools to do that now. So this idea of how do we scale, how do we scale people and teams is now becoming more important as we scale the technology. And then recognizing it's bounded by context. You know, if I'm in, if I'm working in a hospital and I have a framework where I talk about ask AI, assisted AI, and automated AI. I would, I would not put automated AI in there right now. You know, there is no way because the impact of getting that wrong is, is a bad thing will happen to a human. Um, yes, exactly. If I'm building a demo system, then yeah, I can put it onto auto mode and see what happens because the blast impact, the, the bad, uh, impact if I get it wrong is, is minimal. Um, and so I think that context is really, really important. Context of the team and technology, like you said, you know, uh, we, When we're independent, we get to play with the cool tools because it's our choice. When we work in large organizations, we follow the architecture and that architecture is there for
