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.