  AI agents excel in executing predefined tasks, but the dynamic management of
work state information during task execution remains an underexplored area. We
propose a work state-centric AI agent model employing "work notes" to record
and reflect the state throughout task execution. This paper details the model's
architecture, featuring worker threads for task oversight, planner modules for
task decomposition and planning, and executor modules for performing subtasks
using a ReAct-inspired thought-action loop. We provide an exhaustive work state
record incorporating plans and outcomes, constituting a comprehensive work
journal. Our results show that this model not only improves task execution
efficiency but also lays a solid foundation for subsequent task analysis and
auditing.
