Why agents represent the next AI paradigm in Agentic AI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple AI agent class.

Agentic AI

class Agent:
    def __init__(self, name):
        self.name = [1]

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

Aname

Bagent

Cself

DAI

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'agent' instead of 'name' as the value.

Using 'self' which is the instance, not the parameter.

The agent's name is set by assigning the parameter 'name' to the instance variable self.name.

2fill in blank

medium

Complete the code to make the agent perform an action.

Agentic AI

class Agent:
    def act(self, task):
        print(f"Agent [1] is performing: {task}")

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

Aaction

Btask

Cname

Dself.name

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'task' instead of the agent's name.

Using 'name' which is not defined in this method.

The agent's name is stored in self.name, which is used to identify the agent in the print statement.

3fill in blank

hard

Fix the error in the agent's decision method to return the correct action.

Agentic AI

class Agent:
    def decide(self, options):
        best_option = max(options, key=lambda x: x['score'])
        return best_option[1]

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

A['score']

B['action']

C.action

D.score

Attempts:

3 left

💡 Hint

Common Mistakes

Using dot notation which does not work for dictionaries.

Returning the score instead of the action.

The best option is a dictionary, so to get the action, use best_option['action'].

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps tasks to their priorities if priority is above 5.

Agentic AI

tasks = ['clean', 'cook', 'shop']
priorities = {'clean': 7, 'cook': 4, 'shop': 9}
high_priority = {task: priorities[task] for task in tasks if priorities[task] [1] [2]

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

A>

B5

C<

D7

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' causing wrong filtering.

Using 7 instead of 5 as the threshold.

We want tasks with priority greater than 5, so use '>' and 5.

5fill in blank

hard

Fill all three blanks to create a dictionary of task names in uppercase mapped to their priority if priority is less than 8.

Agentic AI

tasks = ['read', 'write', 'code']
priorities = {'read': 6, 'write': 8, 'code': 7}
filtered_tasks = {task[1]: priorities[task] for task in tasks if priorities[task] [2] [3]

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

A.upper()

B<

C8

D.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using .lower() instead of .upper() for task names.

Using '>' instead of '<' for filtering priorities.

We convert task names to uppercase with .upper(), and filter priorities less than 8 using '<' and 8.

Practice

1. What is the main reason agents are considered the next AI paradigm?

easy

A. They work without any input or feedback from the environment.

B. They only store large amounts of data efficiently.

C. They replace all traditional programming languages.

D. They can perceive, decide, and act to solve tasks autonomously.

Solution

1. Step 1: Understand what agents do

   Agents perceive their environment, make decisions, and take actions to solve tasks.

2. Step 2: Compare options to agent capabilities

   Only They can perceive, decide, and act to solve tasks autonomously. correctly describes this autonomous behavior; others are incorrect or unrelated.

3. Final Answer:

   They can perceive, decide, and act to solve tasks autonomously. -> Option D

4. Quick Check:

   Agent autonomy = They can perceive, decide, and act to solve tasks autonomously. [OK]

Hint: Agents act autonomously by perceiving and deciding [OK]

Common Mistakes:

• Thinking agents only store data
• Believing agents need no input
• Confusing agents with programming languages

2. Which of the following is the correct way to describe an agent's decision process?

easy

A. An agent randomly chooses actions without input.

B. An agent only stores past actions without planning.

C. An agent perceives input, plans, then acts.

D. An agent acts before perceiving the environment.

Solution

1. Step 1: Recall agent decision steps

   Agents first perceive their environment, then plan decisions, and finally act.

2. Step 2: Match options to this process

   Only An agent perceives input, plans, then acts. correctly states the sequence: perceive, plan, act.

3. Final Answer:

   An agent perceives input, plans, then acts. -> Option C

4. Quick Check:

   Decision process = perceive, plan, act [OK]

Hint: Agents perceive first, then plan and act [OK]

Common Mistakes:

• Assuming agents act randomly
• Thinking agents act before perceiving
• Ignoring the planning step

3. Consider this simple agent code snippet:

class Agent:
    def __init__(self):
        self.state = 0
    def perceive(self, input):
        self.state += input
    def act(self):
        return self.state * 2

agent = Agent()
agent.perceive(3)
agent.perceive(2)
output = agent.act()

What is the value of output after running this code?

medium

A. 10

B. 0

C. 6

D. 5

Solution

1. Step 1: Track the agent's state changes

   Initially, state = 0. After perceive(3), state = 3. After perceive(2), state = 5.

2. Step 2: Calculate the action output

   act() returns state * 2 = 5 * 2 = 10.

3. Final Answer:

   10 -> Option A

4. Quick Check:

   State sum 5 * 2 = 10 [OK]

Hint: Sum inputs before doubling output [OK]

Common Mistakes:

• Using only last input instead of sum
• Forgetting to multiply by 2
• Confusing initial state as output

4. The following agent code has a bug:

class Agent:
    def __init__(self):
        self.state = 0
    def perceive(self, input):
        self.state = input
    def act(self):
        return self.state * 2

agent = Agent()
agent.perceive(3)
agent.perceive(2)
output = agent.act()

What is the bug and how to fix it?

medium

A. Bug: perceive overwrites state; fix by adding input to state.

B. Bug: act returns wrong value; fix by returning state + 2.

C. Bug: __init__ missing; fix by adding __init__ method.

D. Bug: perceive missing; fix by adding perceive method.

Solution

1. Step 1: Identify the bug in perceive method

   perceive sets state = input, overwriting previous state instead of accumulating.

2. Step 2: Fix by accumulating inputs

   Change perceive to add input to state: self.state += input.

3. Final Answer:

   Bug: perceive overwrites state; fix by adding input to state. -> Option A

4. Quick Check:

   Accumulate inputs in perceive [OK]

Hint: Check if state accumulates or overwrites inputs [OK]

Common Mistakes:

• Changing act method instead of perceive
• Adding missing methods not needed here
• Ignoring state update logic

5. Why do agents better handle complex, changing problems compared to traditional AI models?

hard

A. Because agents only memorize fixed rules without adapting.

B. Because agents can plan, adapt, and act continuously in dynamic environments.

C. Because agents ignore environment changes to stay stable.

D. Because agents require no input data to function.

Solution

1. Step 1: Understand agent capabilities in complex environments

   Agents perceive changes, plan accordingly, and adapt their actions continuously.

2. Step 2: Compare with traditional AI limitations

   Traditional AI often uses fixed rules and lacks continuous adaptation, unlike agents.

3. Final Answer:

   Because agents can plan, adapt, and act continuously in dynamic environments. -> Option B

4. Quick Check:

   Adaptation and planning = Because agents can plan, adapt, and act continuously in dynamic environments. [OK]

Hint: Agents adapt and plan in changing environments [OK]

Common Mistakes:

• Thinking agents memorize fixed rules
• Believing agents ignore environment
• Assuming agents work without input