Division of labor for AI Agents will be critical for maximizing the impact of agents in all areas of knowledge work. We've long had a division of labor in organizations because it turns out having individual experts handing off tasks to each other is more effective than a bunch of generalists trying to do things a different way each time. AI Agents present the same dynamic. For AI Agents to work, you need just the right amount of context about the task that they're trying to complete. This means a deep domain understanding, set of knowledge to work off of, clear instructions, and set of tools to use. Too little context and the agent will fail. Yet, equally, as more of this information enters the context window, we know that the models can become suboptimal.  For a complex business process, if you put all of the documentation, description of the workflow, and instructions into the context window, we know that the agent will eventually get confused and deliver worse results. The logical architecture then in the future is to divide agents up in atomic units that map to the right types of tasks and then have these agents working together to complete their work. We're already seeing this play out effectively in coding agents. There are more and more examples emerging with people setting up subagents that all own specific parts of a codebase or service area. Each agent is responsible for a part of the code, and there is agent-friendly documentation for the code. Then as work is needed in that relevant area of the codebase, an orchestrator agent coordinates with these subagents.  We could see this pattern likely applying to almost any area of knowledge work in the future. This will allow AI Agents to be used for far more than task-specific use-cases and extend to powering entire workflows in the enterprise. Even as AI models improve to be able to handle larger context windows, and the intelligence levels go up, it’s not obvious that this architecture ever goes away. It’s likely that the role of each agent expands as capabilities improve, but clear lines of separation between subagents may always lead to better outcomes.