Single agent vs multi-agent systems in Agentic AI - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key difference between single-agent and multi-agent systems

Which statement best describes the main difference between single-agent and multi-agent systems?

A Single-agent systems involve one decision maker, while multi-agent systems involve multiple interacting decision makers.

B Single-agent systems always use supervised learning, while multi-agent systems use unsupervised learning.

C Single-agent systems cannot learn, but multi-agent systems can learn from data.

D Single-agent systems require no environment, but multi-agent systems require a complex environment.

💡 Hint

Think about how many decision makers or 'agents' are involved in each system.

Single-agent systems have one agent making decisions alone. Multi-agent systems have multiple agents that interact and make decisions together or competitively.

❓ Model Choice

intermediate

Choosing the right system for a task

You want to build a system where multiple robots collaborate to clean a large building efficiently. Which system type is most suitable?

A Multi-agent system where robots work independently without any coordination.

B Single-agent system with one robot controlling all cleaning tasks.

C Single-agent system with a central controller but no communication between robots.

D Multi-agent system where each robot acts as an independent agent coordinating with others.

💡 Hint

Consider the benefits of collaboration and communication among multiple robots.

A multi-agent system with coordination allows robots to share tasks and avoid overlap, improving efficiency.

❓ Metrics

advanced

Evaluating multi-agent system performance

Which metric is most appropriate to evaluate cooperation effectiveness in a multi-agent system?

A Individual agent accuracy on isolated tasks.

B Total cumulative reward achieved by all agents working together.

C Training time of a single agent.

D Number of agents in the system.

💡 Hint

Think about measuring how well agents perform as a team.

Total cumulative reward reflects how well agents cooperate to achieve a shared goal.

🔧 Debug

advanced

Identifying a problem in multi-agent communication

In a multi-agent system, agents are supposed to share their states to coordinate. However, agents act as if they have no information about others. What is the most likely cause?

A Agents have no communication protocol implemented.

B Agents are using supervised learning instead of reinforcement learning.

C The environment is too simple for communication to matter.

D Agents are overfitting their local data.

💡 Hint

If agents don't know about others, what feature might be missing?

Without a communication protocol, agents cannot share information, so they behave independently.

❓ Predict Output

expert

Output of multi-agent reward calculation code

What is the output of the following Python code simulating rewards for two agents cooperating?

Agentic AI

agents = ['A1', 'A2']
rewards = {'A1': [1, 2, 3], 'A2': [2, 2, 2]}
cumulative_rewards = {agent: sum(rewards[agent]) for agent in agents}
total_reward = sum(cumulative_rewards.values())
print(total_reward)

A 9

B 10

C 12

D 6

💡 Hint

Add the rewards for each agent, then sum both totals.

Agent A1's rewards sum to 6 (1+2+3), A2's sum to 6 (2+2+2), total is 12.

Practice

1. What is the main difference between a single agent system and a multi-agent system?

easy

A. A single agent system has one decision-maker, while a multi-agent system has multiple interacting agents.

B. A single agent system always uses deep learning, multi-agent systems do not.

C. Multi-agent systems cannot communicate, single agent systems can.

D. Single agent systems require more computing power than multi-agent systems.

Solution

1. Step 1: Understand agent count in systems

   Single agent systems have exactly one agent making decisions alone.

2. Step 2: Understand interaction in multi-agent systems

   Multi-agent systems have multiple agents that interact and cooperate or compete.

3. Final Answer:

   A single agent system has one decision-maker, while a multi-agent system has multiple interacting agents. -> Option A

4. Quick Check:

   Agent count and interaction define system type = A [OK]

Hint: Count agents: one means single, many means multi-agent [OK]

Common Mistakes:

• Confusing communication ability with agent count
• Thinking single agent systems always use deep learning
• Assuming multi-agent systems cannot communicate

2. Which of the following is the correct way to describe a multi-agent system?

easy

A. A system where one agent acts without any interaction.

B. A system that only uses a single neural network.

C. A system with multiple agents that can interact and collaborate.

D. A system that cannot learn from the environment.

Solution

1. Step 1: Identify multi-agent system traits

   Multi-agent systems have multiple agents that interact or collaborate.

2. Step 2: Eliminate incorrect options

   Descriptions of single agent without interaction, single neural network usage, or inability to learn are incorrect.

3. Final Answer:

   A system with multiple agents that can interact and collaborate. -> Option C

4. Quick Check:

   Multiple interacting agents = multi-agent system = C [OK]

Hint: Look for multiple interacting agents to spot multi-agent systems [OK]

Common Mistakes:

• Choosing single agent descriptions for multi-agent questions
• Confusing neural network use with agent count
• Assuming multi-agent systems cannot learn

3. Consider this Python code simulating agents' decisions:

class Agent:
    def __init__(self, name):
        self.name = name
    def act(self):
        return f"{self.name} acts alone"

agents = [Agent("A1"), Agent("A2")]
results = [agent.act() for agent in agents]
print(results)

What is the output?

medium

A. Error: Agent class missing act method

B. ['A1 acts alone']

C. ['acts alone', 'acts alone']

D. ['A1 acts alone', 'A2 acts alone']

Solution

1. Step 1: Understand the Agent class and act method

   Each Agent has a name and act() returns a string with that name plus 'acts alone'.

2. Step 2: List comprehension calls act() for each agent

   Two agents: 'A1' and 'A2', so results list has two strings with their names.

3. Final Answer:

   ['A1 acts alone', 'A2 acts alone'] -> Option D

4. Quick Check:

   Each agent acts alone string collected = A [OK]

Hint: List comprehension calls act() on each agent, so output matches agent names [OK]

Common Mistakes:

• Assuming only one agent acts
• Ignoring the agent name in the output string
• Thinking act method is missing

4. The following code is intended to simulate a multi-agent system where agents share their actions. What is the error?

class Agent:
    def __init__(self, name):
        self.name = name
    def act(self):
        return f"{self.name} acts"

agents = [Agent("A1"), Agent("A2")]
actions = []
for agent in agents:
    actions.append(agent.act)
print(actions)

medium

A. Agent class is missing the __init__ method.

B. The act method is not called; missing parentheses in append.

C. The list 'actions' should be a dictionary.

D. The print statement is outside the loop causing an error.

Solution

1. Step 1: Check how act method is used in the loop

   actions.append(agent.act) adds the method itself, not its result.

2. Step 2: Fix by calling the method with parentheses

   Use actions.append(agent.act()) to add the returned string.

3. Final Answer:

   The act method is not called; missing parentheses in append. -> Option B

4. Quick Check:

   Method call needs () to execute = B [OK]

Hint: Remember to call methods with () to get results, not the method itself [OK]

Common Mistakes:

• Appending method reference instead of calling it
• Thinking __init__ is missing when it is present
• Assuming print outside loop causes error

5. You want to design a system where multiple robots explore a building and share information to avoid collisions. Which system type fits best and why?

hard

A. Multi-agent system, because multiple robots interact and share information.

B. Multi-agent system, but agents act completely independently without sharing.

C. Single agent system, because robots do not need to communicate.

D. Single agent system, because one robot controls all decisions centrally.

Solution

1. Step 1: Analyze problem needs for multiple robots

   Multiple robots exploring means multiple agents acting simultaneously.

2. Step 2: Consider interaction and information sharing

   To avoid collisions, robots must share info and coordinate, needing interaction.

3. Final Answer:

   Multi-agent system, because multiple robots interact and share information. -> Option A

4. Quick Check:

   Multiple interacting agents sharing info = multi-agent system = D [OK]

Hint: Multiple robots sharing info means multi-agent system [OK]

Common Mistakes:

• Choosing single agent for multiple robots
• Ignoring need for communication to avoid collisions
• Thinking multi-agent means no interaction