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My first GTD agents were not very good. Actually, from a GTD (Getting Things Done) workflow perspective, some of them were doing complete nonsense. I had already framed them carefully. I was using my usual Prompt Constitution / Playbook approach: clear role, clear mission, operating rules, tool boundaries, expected outputs, validation steps. And still, the agent started doing things that looked productive on the surface but were absolutely wrong from a GTD perspective. The worst example was my calendar. The agent was processing Outlook emails and, whenever it detected something that looked remotely time-related, it started creating calendar events. Not real meetings. Pseudo-meetings. Sometimes partially renamed from the email subject line. Sometimes duplicated several times. And if the AI found open space in my calendar, it seemed happy to fill it. From the outside, this looked like automation. From a GTD perspective, it was a system failure. Because an email that mentions a date is not automatically a calendar item. An email that requires a response is not automatically a task. An email that refers to a project is not automatically a project. And an AI agent that can access your tools but does not understand your GTD decision model is not an assistant. It is a very fast source of trusted-system pollution. That was the turning point for me. I realized that building a useful GTD agent does not start with choosing the best model. It starts with defining the exact workflow the agent is allowed to execute. - Not the dream workflow. - Not “AI handles my inbox.” - Not “AI creates tasks from emails.” The real question is: What GTD decision model should the agent follow? For example, if the agent is helping process an Outlook inbox, its mission should not be: “Read my emails and create tasks.” That is far too vague. The mission should be: Turn each incoming email into a reliable GTD decision. That changes everything. Because in GTD, an email can become many different things: