Taming the AI Taskmaster: In Search of Integrated and Flexible Project Management for AI-Driven Workflows
As AI becomes more integral to the development process, effectively managing the tasks it undertakes—and the tasks we delegate to it—becomes paramount. My journey, as detailed in previous posts on AI’s evolving landscape and crafting AI rules, naturally led to the question of task management. How do we best define, assign, track, and adapt tasks when an AI is a key collaborator?
The Promise and Pitfalls of Dedicated AI Task Managers
One early exploration was the task-master project. The concept, and its impressive landing page, certainly caught my attention. The idea of having clearly numbered tasks with proper descriptions is undoubtedly appealing. However, in practice, I encountered several limitations:
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PRD-Centricity:
task-masterprimarily bases its work on a Product Requirements Document (PRD). As I’ve discussed, a PRD is often a static, human-oriented document. For AI collaboration, a dynamic, AI-centric “memory bank” feels more appropriate for ongoing context and evolving requirements. - Lack of Integration: I found myself wishing for task triggering and management within the AI agent’s environment (e.g., my code editor), rather than using a separate chat interface disconnected from the immediate code and context.
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LLM Constraints: At the time of my review,
task-masterwas bound to Anthropic’s LLMs, a limitation I preferred to avoid. - Over-Engineered Features: Features like detailed task dependency management and ETA calculations, while perhaps useful in traditional project management, felt like overkill for my AI-assisted workflow. The dynamic nature of AI contributions often makes such rigid planning less effective.
- Alternative Tooling: For visual task management like Kanban boards, tools like Notion or Jira, which can be populated with a single prompt, often provide a more user-friendly and feature-rich experience.
When the AI Wants to Create Its Own Tasks
The need for better task integration became even clearer during an experience with Gemini 2.5 Pro. The model, after completing a task, astutely identified that some existing code could be improved with better naming. I agreed, and it made the changes. However, when it came time to commit this refactoring, a problem arose: no task had been explicitly created for this emergent piece of work in the memory bank. The AI, lacking a directive or a mechanism to create one, was stuck.
This incident was a 
moment that pushed me to re-examine the cursor-memory-bank documentation more thoroughly. I was looking for how task creation was handled, only to find it wasn’t a core documented feature for such dynamic scenarios. It highlighted that many existing tools were perhaps designed with older, less proactive LLMs in mind, or focused more on initial project bootstrapping rather than ongoing, adaptive collaboration.
Back to Fundamentals: Flexible, Not Over-Engineered
These experiences led me back towards the foundational principles of the Cline Memory Bank. Its philosophy doesn’t attempt to over-engineer the entire process from A to Z or predict every intermediate step (like points R, T, F, or M in a grand plan) from the outset.
While this approach admittedly requires more manual effort in crafting rules and workflows, the payoff is greater flexibility and robustness. Instead of trying to fit the AI into a rigid, pre-defined task structure, the goal is to create a system where tasks can be defined, refined, and even generated more organically as the project evolves and the AI contributes insights. The focus shifts from exhaustive upfront planning of minutiae to a more agile interaction, where the AI can be a partner in both execution and task definition.
The journey continues, but the lesson is clear: for AI task management, less rigid prediction and more adaptive, integrated mechanisms are key.