Agents are designed to act independently and handle multiple tasks, while chatbots typically focus on answering questions or following simple scripts.
Agents can take independent actions in their environment, while chatbots usually wait for user input before responding.
Reinforcement learning models enable agents to learn by interacting with their environment and planning actions, unlike static chatbot models.
Task completion rate measures how well an agent achieves goals over time, which is less relevant for chatbots focused on single-turn replies.
The agent's act method depends on tasks added by perceive, but perceive was never called, so no tasks exist to execute.
