How agents differ from chatbots in Agentic AI - Practice Exercises

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key difference between agents and chatbots

Which statement best describes how agents differ from chatbots?

A Chatbots operate without any predefined rules, but agents rely solely on fixed scripts.

B Chatbots can learn from data over time, but agents cannot adapt after deployment.

C Agents only respond to text input, whereas chatbots can handle voice commands.

D Agents can perform multiple tasks autonomously, while chatbots mainly respond to user queries.

💡 Hint

Think about autonomy and task handling capabilities.

Agents are designed to act independently and handle multiple tasks, while chatbots typically focus on answering questions or following simple scripts.

🧠 Conceptual

intermediate

Agent capabilities beyond chatbots

Which capability is unique to agents compared to chatbots?

A Providing scripted greetings to users.

B Answering frequently asked questions quickly.

C Executing actions in the environment without user prompts.

D Translating text from one language to another.

💡 Hint

Consider which system can act on its own initiative.

Agents can take independent actions in their environment, while chatbots usually wait for user input before responding.

❓ Model Choice

advanced

Choosing a model for an agent vs chatbot

You want to build a system that can autonomously plan and execute tasks over time. Which model type suits this better?

A Rule-based chatbot with fixed responses.

B Reinforcement learning model with environment interaction.

C Simple sequence-to-sequence model for text generation.

D Statistical language model trained only on chat logs.

💡 Hint

Think about models that learn from actions and consequences.

Reinforcement learning models enable agents to learn by interacting with their environment and planning actions, unlike static chatbot models.

❓ Metrics

advanced

Evaluating agent vs chatbot performance

Which metric is more appropriate to evaluate an agent's success compared to a chatbot?

A Task completion rate over multiple steps.

B Accuracy of sentiment classification in replies.

C Number of words generated per response.

D Response time to user messages.

💡 Hint

Consider what shows an agent's ability to finish tasks.

Task completion rate measures how well an agent achieves goals over time, which is less relevant for chatbots focused on single-turn replies.

🔧 Debug

expert

Debugging agent behavior in code

Given this simplified agent code snippet, what is the main reason the agent fails to act autonomously? ```python class Agent: def __init__(self): self.tasks = [] def perceive(self, input): self.tasks.append(input) def act(self): if self.tasks: task = self.tasks.pop(0) print(f"Executing {task}") else: print("No tasks to perform") agent = Agent() agent.act() ``` Options:

A The agent never receives any input before acting, so tasks list is empty.

B The act method has a syntax error causing runtime failure.

C The tasks list is never initialized in the constructor.

D The perceive method removes tasks instead of adding them.

💡 Hint

Check how the agent gets tasks before acting.

The agent's act method depends on tasks added by perceive, but perceive was never called, so no tasks exist to execute.