How agents differ from chatbots in Agentic AI - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a simple chatbot response function.

Agentic AI

def chatbot_response(user_input):
    return "Hello! You said: " + user_input[1]

Drag options to blanks, or click blank then click option'

A-

B/

C*

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using arithmetic operators like - or * instead of + for strings.

The chatbot_response function concatenates the greeting string with the user input using the + operator.

2fill in blank

medium

Complete the code to add a simple agent action method that decides what to do based on input.

Agentic AI

class Agent:
    def decide_action(self, observation):
        if observation == 'greet':
            return 'say_hello'
        else:
            return [1]

Drag options to blanks, or click blank then click option'

A'wait'

B'do_nothing'

C'say_hello'

D'ignore'

Attempts:

3 left

💡 Hint

Common Mistakes

Returning the same action for all inputs, which ignores the condition.

The agent returns 'do_nothing' when the observation is not 'greet', showing a simple decision process.

3fill in blank

hard

Fix the error in the agent's method that should update its internal state.

Agentic AI

class Agent:
    def __init__(self):
        self.state = 0
    def update_state(self, observation):
        self.state = self.state [1] 1

Drag options to blanks, or click blank then click option'

A+

B-

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using subtraction or multiplication which changes the state incorrectly.

The update_state method increments the agent's state by 1 using the + operator.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps observations to actions only if the observation is 'task'.

Agentic AI

actions = {obs: 'execute' for obs in observations if obs [1] [2]

Drag options to blanks, or click blank then click option'

A==

B'task'

C'greet'

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' instead of '==' which would select the wrong observations.

The comprehension filters observations equal to 'task' and maps them to the action 'execute'.

5fill in blank

hard

Fill all three blanks to create a function that returns a dictionary of agent decisions for each input observation if the observation length is greater than 3.

Agentic AI

def agent_decisions(observations):
    return {obs[1]: 'act' for obs in observations if len(obs) [2] [3]

Drag options to blanks, or click blank then click option'

A.upper()

B>

C3

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' which filters the wrong observations.

The function maps each observation converted to uppercase to 'act' only if its length is greater than 3.

Practice

1. What is the main difference between an agent and a chatbot?

easy

A. Agents only chat, chatbots can act on tasks.

B. Chatbots can perform tasks, but agents only respond with text.

C. Agents can plan and perform multiple tasks, while chatbots mainly focus on chatting.

D. There is no difference; both are the same.

Solution

1. Step 1: Understand agent capabilities

   Agents are designed to plan and perform various tasks beyond just chatting.

2. Step 2: Understand chatbot capabilities

   Chatbots mainly focus on conversation and do not perform complex actions.

3. Final Answer:

   Agents can plan and perform multiple tasks, while chatbots mainly focus on chatting. -> Option C

4. Quick Check:

   Main difference = Agents act, chatbots chat [OK]

Hint: Agents do tasks; chatbots just chat [OK]

Common Mistakes:

• Thinking chatbots can perform complex tasks
• Believing agents only chat
• Assuming no difference between them

2. Which of the following is a correct statement about agents in AI?

easy

A. Agents can plan steps and execute tasks automatically.

B. Agents only respond to user messages without performing actions.

C. Agents cannot remember past interactions.

D. Agents are limited to simple keyword matching.

Solution

1. Step 1: Review agent abilities

   Agents are designed to plan and carry out tasks automatically.

2. Step 2: Eliminate incorrect options

   Options B, C, and D describe chatbots or limited systems, not agents.

3. Final Answer:

   Agents can plan steps and execute tasks automatically. -> Option A

4. Quick Check:

   Agents plan and act = A [OK]

Hint: Agents plan and act automatically [OK]

Common Mistakes:

• Confusing agents with simple chatbots
• Thinking agents only reply without action
• Assuming agents lack memory

3. Consider this code snippet for an AI system:

class SimpleChatbot:
    def respond(self, message):
        return "Hello! How can I help?"

class Agent:
    def plan(self, goal):
        return ["Step 1", "Step 2", "Step 3"]
    def execute(self, steps):
        return "Tasks done"

bot = SimpleChatbot()
agent = Agent()
print(bot.respond("Hi"))
print(agent.plan("Clean room"))
print(agent.execute(agent.plan("Clean room")))

What is the output of this code?

medium

A. "Hello! How can I help?"
["Step 1", "Step 2", "Step 3"]
"Tasks done"

B. "Hi"
"Clean room"
"Done"

C. Error because Agent has no respond method

D. "Hello! How can I help?"
"Clean room"
"Step 1, Step 2, Step 3"

Solution

1. Step 1: Analyze SimpleChatbot respond method

   Calling respond("Hi") returns the fixed string "Hello! How can I help?".

2. Step 2: Analyze Agent plan and execute methods

   plan("Clean room") returns the list ["Step 1", "Step 2", "Step 3"]. execute(...) returns "Tasks done".

3. Final Answer:

   "Hello! How can I help?" ["Step 1", "Step 2", "Step 3"] "Tasks done" -> Option A

4. Quick Check:

   Chatbot replies, agent plans and executes [OK]

Hint: Chatbot replies fixed text; agent returns plan and done [OK]

Common Mistakes:

• Assuming agent has respond method
• Confusing plan output with execute output
• Expecting error due to missing respond in Agent

4. The following code tries to use an agent to chat but fails:

class Agent:
    def plan(self, goal):
        return ["Step 1", "Step 2"]

agent = Agent()
print(agent.respond("Hello"))

What is the error and how to fix it?

medium

A. No error; code runs fine.

B. SyntaxError due to missing colon; add colon after plan method.

C. TypeError because respond needs two arguments; add self parameter.

D. AttributeError because Agent has no respond method; add respond method to Agent.

Solution

1. Step 1: Identify error from code

   Calling agent.respond("Hello") causes AttributeError because Agent class lacks respond method.

2. Step 2: Fix by adding respond method

   To fix, define a respond method inside Agent class that handles chat messages.

3. Final Answer:

   AttributeError because Agent has no respond method; add respond method to Agent. -> Option D

4. Quick Check:

   Missing method causes AttributeError [OK]

Hint: Check if method exists before calling [OK]

Common Mistakes:

• Thinking it's a syntax error
• Confusing method parameters
• Assuming code runs without respond method

5. You want to build an AI system that can chat with users and also book appointments automatically. Which approach best fits this need?

hard

A. Use a chatbot only, since it can handle all tasks.

B. Use an agent that can plan booking steps and chat with users.

C. Use a simple rule-based system without AI.

D. Use a chatbot combined with manual human booking.

Solution

1. Step 1: Understand task requirements

   The system must chat and perform automatic booking, which requires planning and action.

2. Step 2: Match capabilities to approach

   Agents can plan and execute tasks like booking, while chatbots mainly chat.

3. Final Answer:

   Use an agent that can plan booking steps and chat with users. -> Option B

4. Quick Check:

   Complex tasks need agents, not just chatbots [OK]

Hint: Complex tasks need agents, simple chat needs chatbots [OK]

Common Mistakes:

• Choosing chatbot only for complex tasks
• Ignoring automation needs
• Relying on manual steps unnecessarily