How a Shared Task-State File Keeps AI Agent Work From Going Off the Rails

A developer working with an AI agent on a blog post found that small follow-up questions repeatedly pulled both parties away from the original task into nested investigations. Drawing on the concept of a call stack from programming, the author argues that a shared markdown file tracking the current task frame, working hypotheses, and return steps can preserve context across these tangents. Unlike an agent's internal to-do tracker, which may not survive session resets, this external file acts as a mutual handoff surface between the human's and agent's attention. The author notes that drift is not caused solely by the agent — human curiosity and mid-task interruptions are equally responsible for losing the original thread. Maintaining a checkpoint before any focus shift, the piece concludes, makes deep dives reversible rather than derailing.