Real-world agent applications in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to initialize a real-world agent with a task description.

Agentic AI

agent = RealWorldAgent(task_description=[1])

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

A12345

BNone

CTrue

D"Schedule meetings"

Attempts:

3 left

💡 Hint

Common Mistakes

Using a number instead of a string

Passing None instead of a description

The task_description should be a string describing the agent's task, such as "Schedule meetings".

2fill in blank

medium

Complete the code to make the agent perform its task and return the result.

Agentic AI

result = agent.[1]()

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

Aexecute

Breset

Cinitialize

Dstop

Attempts:

3 left

💡 Hint

Common Mistakes

Using initialize instead of execute

Using stop which ends the agent

The method to perform the agent's task is usually called execute or run; here, execute is correct.

3fill in blank

hard

Fix the error in the code to correctly check if the agent completed the task.

Agentic AI

if agent.status == [1]:
    print("Task completed")

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

A"done"

B1

C"completed"

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Using True instead of 'completed'

Using 'done' which is incorrect

The agent's status is a string 'completed' when the task is done, so the check must be against "completed".

4fill in blank

hard

Fill both blanks to create a dictionary of task results where keys are task names and values are their statuses.

Agentic AI

task_results = {task: agent.[1](task) for task in tasks if agent.[2](task)}

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

Aget_status

Bis_active

Crun_task

Dstop_task

Attempts:

3 left

💡 Hint

Common Mistakes

Using run_task instead of get_status

Using stop_task instead of is_active

get_status(task) returns the status, and is_active(task) checks if the task is active.

5fill in blank

hard

Fill all three blanks to filter tasks with priority above 5 and create a summary dictionary with task names and priorities.

Agentic AI

summary = {task[1]: agent.get_priority([2]) for task in tasks if agent.get_priority(task) [3] 5}

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

A.upper()

Btask

C>

Dtask.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using .upper() instead of .lower()

Using < instead of > in filter

task.lower() is used for the key, task for the get_priority argument, and the filter uses > 5.

Practice

1. What is the main role of a real-world agent in AI applications?

easy

A. To only observe without making decisions

B. To store large amounts of data without interaction

C. To sense the environment and act to achieve goals

D. To randomly perform actions without purpose

Solution

1. Step 1: Understand agent behavior

   Real-world agents sense their surroundings and make decisions based on what they observe.

2. Step 2: Connect sensing and acting

   Agents act to reach specific goals, not randomly or passively.

3. Final Answer:

   To sense the environment and act to achieve goals -> Option C

4. Quick Check:

   Agent role = sensing + acting [OK]

Hint: Agents always sense and act to reach goals [OK]

Common Mistakes:

• Thinking agents only observe without acting
• Believing agents act randomly
• Confusing data storage with agent action

2. Which code snippet correctly represents the agent loop in Python?

easy

A. while False:
decide()
observe()
act()

B. for i in range(3):
act()
decide()
observe()

C. if observe():
act()
decide()

D. while True:
observe()
decide()
act()

Solution

1. Step 1: Identify the correct loop structure

   The agent loop runs continuously, so a while True loop is appropriate.

2. Step 2: Check the order of actions

   The correct order is observe, then decide, then act.

3. Final Answer:

   while True:\n observe()\n decide()\n act() -> Option D

4. Quick Check:

   Loop + observe-decide-act order = while True: observe() decide() act() [OK]

Hint: Agent loop is infinite with observe, decide, then act [OK]

Common Mistakes:

• Using for loop instead of infinite loop
• Wrong order of observe, decide, act
• Loop condition that never runs

3. Given this agent code snippet, what will be printed?

def observe():
    return 'rainy'
def decide(weather):
    return 'take umbrella' if weather == 'rainy' else 'no umbrella'
def act(action):
    print(f'Action: {action}')

weather = observe()
action = decide(weather)
act(action)

medium

A. Action: no umbrella

B. Action: take umbrella

C. Action: sunny

D. No output

Solution

1. Step 1: Trace the observe function

   observe() returns 'rainy'.

2. Step 2: Trace the decide function

   decide('rainy') returns 'take umbrella' because weather is 'rainy'.

3. Step 3: Trace the act function

   act('take umbrella') prints 'Action: take umbrella'.

4. Final Answer:

   Action: take umbrella -> Option B

5. Quick Check:

   observe='rainy' -> decide='take umbrella' -> print output [OK]

Hint: Follow data flow: observe -> decide -> act output [OK]

Common Mistakes:

• Ignoring the condition in decide()
• Confusing output text
• Assuming no print happens

4. Find the error in this agent loop code:

while True:
    action = decide(observe)
    act(action)

medium

A. observe should be called as observe()

B. act() should return a value

C. decide() should not take any arguments

D. while True should be replaced with for loop

Solution

1. Step 1: Check function calls

   observe is passed without parentheses, so it's a function object, not its result.

2. Step 2: Correct function call

   observe() should be called to get the observed data before passing to decide.

3. Final Answer:

   observe should be called as observe() -> Option A

4. Quick Check:

   Function call missing parentheses = observe should be called as observe() [OK]

Hint: Call functions with () to get results [OK]

Common Mistakes:

• Passing function object instead of calling it
• Expecting act() to return value
• Changing loop type unnecessarily

5. You want to build an agent that automatically trades stocks based on price trends. Which sequence best describes the agent's real-world loop?

hard

A. Observe stock prices -> Decide buy/sell -> Act by placing orders

B. Act by placing orders -> Observe stock prices -> Decide buy/sell

C. Decide buy/sell -> Act by placing orders -> Observe stock prices

D. Observe stock prices -> Act by placing orders -> Decide buy/sell

Solution

1. Step 1: Understand agent loop order

   The agent must first observe the environment (stock prices) before deciding.

2. Step 2: Confirm correct action order

   After deciding buy or sell, the agent acts by placing orders.

3. Final Answer:

   Observe stock prices -> Decide buy/sell -> Act by placing orders -> Option A

4. Quick Check:

   Observe -> Decide -> Act is standard agent loop [OK]

Hint: Agent loop always: observe, then decide, then act [OK]

Common Mistakes:

• Mixing up the order of observe, decide, act
• Thinking action happens before decision
• Ignoring environment sensing step