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Agentic AIml~20 mins

Real-world agent applications in Agentic AI - Practice Problems & Coding Challenges

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Challenge - 5 Problems
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Real-World Agent Master
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Test your skills under time pressure!
🧠 Conceptual
intermediate
2:00remaining
Understanding Agent Roles in Real-World Applications

Which of the following best describes the primary role of an agent in a real-world AI application?

AAn agent only processes data offline without any decision-making
BAn agent passively stores data without interacting with the environment
CAn agent actively perceives its environment and takes actions to achieve goals
DAn agent is a static model that never updates or learns from new data
Attempts:
2 left
💡 Hint

Think about how a self-driving car senses and reacts to its surroundings.

❓ Model Choice
intermediate
2:00remaining
Choosing the Right Agent Model for a Delivery Robot

You are designing an AI agent for a delivery robot that must navigate city streets, avoid obstacles, and deliver packages on time. Which agent architecture is most suitable?

ASimple reflex agent that acts only on current sensor input
BModel-based reflex agent that maintains internal state about the environment
CStatic lookup table agent with fixed responses
DRandom agent that chooses actions randomly
Attempts:
2 left
💡 Hint

Consider the need to remember past information to make better decisions.

❓ Metrics
advanced
2:00remaining
Evaluating Agent Performance in a Dynamic Environment

An AI agent is deployed in a warehouse to pick and place items. Which metric best measures how efficiently the agent completes its tasks over time?

AAverage task completion time and success rate combined
BAccuracy of item classification only
CNumber of sensor readings collected
DMemory usage of the agent's software
Attempts:
2 left
💡 Hint

Think about both speed and correctness of task completion.

🔧 Debug
advanced
2:00remaining
Identifying the Cause of Agent Failure in Navigation

An autonomous agent in a smart home fails to reach the kitchen reliably. The code snippet below shows the decision logic. What is the most likely cause of failure?

Agentic AI
def decide_action(sensor_data):
    if sensor_data['obstacle'] == True:
        return 'stop'
    elif sensor_data['destination'] == 'kitchen':
        return 'move_forward'
    else:
        return 'turn_left'
AThe sensor_data dictionary is missing the 'obstacle' key
BThe agent never moves forward because the condition is always false
CThe agent turns left only when the destination is the kitchen
DThe agent stops whenever it detects any obstacle, even if it can navigate around it
Attempts:
2 left
💡 Hint

Consider how the agent reacts to obstacles and if it can continue moving.

❓ Predict Output
expert
2:00remaining
Output of Multi-Agent Interaction Simulation

Consider two agents interacting in a simulation. Agent A shares a message, and Agent B responds based on the message content. What is the output of the following code?

Agentic AI
class Agent:
    def __init__(self, name):
        self.name = name
    def send_message(self, other, message):
        return other.receive_message(message)
    def receive_message(self, message):
        if 'help' in message:
            return f'{self.name} received help request'
        else:
            return f'{self.name} received unknown message'

agent_a = Agent('A')
agent_b = Agent('B')
output = agent_a.send_message(agent_b, 'I need help with task')
print(output)
AB received help request
BA received help request
CB received unknown message
DA received unknown message
Attempts:
2 left
💡 Hint

Trace which agent receives the message and how it processes it.