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.