Computer use agents in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple agent that prints a greeting.

Agentic AI

class SimpleAgent:
    def __init__(self, name):
        self.name = name

    def greet(self):
        print([1])

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

A"Hello, I am " + self.name

BHello, I am agent

Cself.name

Dprint('Hello')

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to use self.name inside the method.

Trying to print a string without concatenating the name.

The greet method should print a greeting that includes the agent's name, so concatenating the string with self.name is correct.

2fill in blank

medium

Complete the code to make the agent decide an action based on input.

Agentic AI

def decide_action(input_text):
    if 'hello' in input_text.lower():
        return [1]
    else:
        return 'ignore'

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

A'responding'

Brespond

C'respond'

D'response'

Attempts:

3 left

💡 Hint

Common Mistakes

Returning a variable name without quotes.

Returning a wrong string that does not match the expected action.

The function should return the string 'respond' when 'hello' is in the input text.

3fill in blank

hard

Fix the error in the agent's learning method to update knowledge correctly.

Agentic AI

class LearningAgent:
    def __init__(self):
        self.knowledge = []

    def learn(self, data):
        self.knowledge.[1](data)

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

Aappend

Badd

Cextend

Dinsert

Attempts:

3 left

💡 Hint

Common Mistakes

Using extend which expects an iterable and causes error if data is not iterable.

Using add which is not a list method.

To add a single data item to the knowledge list, append is the correct method.

4fill in blank

hard

Fill both blanks to create a dictionary of agent names and their scores above 50.

Agentic AI

scores = {'agent1': 45, 'agent2': 75, 'agent3': 60}
high_scores = { [1]: [2] for [1], [2] in scores.items() if [2] > 50 }

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

Aagent

Bscore

Cname

Dscore_value

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not matching the variable names in the comprehension.

The dictionary comprehension uses 'name' as the key variable and 'score' as the value from scores[name].

5fill in blank

hard

Fill all three blanks to filter and transform agent data into a new dictionary.

Agentic AI

agents = {'a1': 30, 'a2': 55, 'a3': 70}
filtered_agents = { [1]: [2]*2 for [1] in agents if agents[[3]] > 50 }

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

Aagent_id

Bagents[agent_id]

Dagent

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not multiplying the value correctly.

The comprehension uses 'agent_id' as the key, doubles the value agents[agent_id], and filters by agents[agent_id] > 50.

Practice

1. What is the main role of a computer use agent?

easy

A. To display graphics on the screen

B. To perform tasks automatically by sensing and acting

C. To store large amounts of data

D. To manually control the computer hardware

Solution

1. Step 1: Understand what an agent does

   An agent senses its environment and takes actions to complete tasks automatically.

2. Step 2: Compare options with this definition

   Only To perform tasks automatically by sensing and acting describes automatic task performance by sensing and acting.

3. Final Answer:

   To perform tasks automatically by sensing and acting -> Option B

4. Quick Check:

   Agent role = automatic task performance [OK]

Hint: Agents act automatically by sensing environment [OK]

Common Mistakes:

• Confusing agents with hardware controllers
• Thinking agents only store data
• Assuming agents only display information

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

easy

A. Sense environment -> Take action -> Update environment

B. Take action -> Sense environment -> Sleep

C. Sense environment -> Sleep -> Take action

D. Update environment -> Take action -> Sense environment

Solution

1. Step 1: Recall the agent cycle steps

   An agent first senses its environment, then takes an action based on that sensing.

2. Step 2: Match the correct sequence

   Sense environment -> Take action -> Update environment correctly shows sensing first, then acting, then environment update.

3. Final Answer:

   Sense environment -> Take action -> Update environment -> Option A

4. Quick Check:

   Agent cycle = sense then act [OK]

Hint: Agents sense first, then act, then update [OK]

Common Mistakes:

• Mixing order of sensing and acting
• Including sleep incorrectly in cycle
• Ignoring environment update step

3. Consider this simple agent code snippet:

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

agent = Agent()
agent.sense(3)
agent.sense(4)
print(agent.act())

What is the output of this code?

medium

A. 14

B. 7

C. 12

D. 0

Solution

1. Step 1: Calculate state after sensing inputs

   Initial state is 0. After agent.sense(3), state = 3. After agent.sense(4), state = 7.

2. Step 2: Calculate action output

   agent.act() returns state * 2 = 7 * 2 = 14.

3. Final Answer:

   14 -> Option A

4. Quick Check:

   State sum 7 x 2 = 14 [OK]

Hint: Add inputs then multiply by 2 for output [OK]

Common Mistakes:

• Multiplying inputs separately instead of sum
• Using only last input instead of sum
• Confusing state update logic

4. This agent code has a bug:

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

agent = Agent()
agent.sense(3)
agent.sense(4)
print(agent.act())

What is the bug and how to fix it?

medium

A. Bug: sense method missing; Fix: add sense method

B. Bug: act returns wrong value; Fix: return state + 2

C. Bug: state overwritten each sense; Fix: use += to accumulate

D. Bug: state not initialized; Fix: initialize state in act

Solution

1. Step 1: Identify the problem in sense method

   The sense method sets state = input, so previous state is lost on each call.

2. Step 2: Fix by accumulating inputs

   Change state = input to state += input to keep adding inputs.

3. Final Answer:

   Bug: state overwritten each sense; Fix: use += to accumulate -> Option C

4. Quick Check:

   Accumulate inputs with += fixes bug [OK]

Hint: Use += to add inputs, not = to overwrite [OK]

Common Mistakes:

• Thinking act method is wrong
• Adding sense method again unnecessarily
• Initializing state in wrong place

5. You want to design a smart agent that automatically adjusts room temperature based on sensor data. Which approach best fits this task?

hard

A. Use a simple reflex agent that acts only on current sensor reading

B. Use a fixed schedule agent ignoring sensor data

C. Use a random agent that changes temperature randomly

D. Use a model-based agent that keeps track of past temperatures

Solution

1. Step 1: Understand task needs

   Adjusting temperature smartly requires remembering past data to avoid sudden changes.

2. Step 2: Choose agent type

   A model-based agent keeps track of past states, making it suitable for this task.

3. Final Answer:

   Use a model-based agent that keeps track of past temperatures -> Option D

4. Quick Check:

   Smart adjustment needs model-based agent [OK]

Hint: Smart agents remember past data for better decisions [OK]

Common Mistakes:

• Choosing simple reflex agent ignoring history
• Using random or fixed schedule agents
• Not considering past sensor data