Work State-Centric AI Agents: Design, Implementation, and Management of Cognitive Work Threads

The burgeoning complexity of tasks that AI agents are expected to perform necessitates a robust framework for managing work states. Traditionally, AI agents have focused on the execution of static tasks without a continuous reflective process on their work state. This limits the agents' ability to manage complex, evolving tasks that require adaptability and nuanced understanding of progress at any given moment. Recognizing the importance of dynamic task management, we introduce a novel AI agent model centered around an explicit work state. The work state captures the entirety of the agent's operational status and provides a medium for recording task evolution-from high-level planning to execution and eventual completion. This state is articulated through "work notes," a concept inspired 1