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Tasks Module

Overview

The tasks module in the AgenticAI Framework manages the creation, scheduling, and execution of tasks for AI agents. It provides a structured way to define workflows, assign responsibilities, and track progress.

Key Classes and Functions

  • Task — Represents a single unit of work with metadata and execution logic.
  • TaskScheduler — Schedules tasks for execution at specific times or intervals.
  • TaskQueue — Manages a queue of pending tasks.
  • execute_task(task_id) — Executes a task by its identifier.
  • list_tasks(status=None) — Lists tasks filtered by their status (pending, in-progress, completed).

Example Usage

from agenticaiframework.tasks import Task, TaskScheduler

# Define a task
task = Task(name="Data Processing", description="Process incoming data files.")

# Initialize scheduler
scheduler = TaskScheduler()
scheduler.add_task(task)

# Execute tasks
scheduler.run_pending()

Use Cases

  • Automating repetitive workflows.
  • Scheduling periodic data processing jobs.
  • Coordinating multi-step AI agent operations.
  • Tracking the status of long-running processes.

Best Practices

  • Keep task definitions modular and reusable.
  • Use descriptive names and metadata for easier tracking.
  • Handle task failures gracefully with retries or fallbacks.
  • Monitor task execution times to identify bottlenecks.