Agent communication protocols in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main purpose of an agent communication protocol?

In multi-agent systems, why do agents use communication protocols?

A To ensure agents understand each other by following agreed rules for message exchange

B To increase the speed of individual agent computations

C To store large amounts of data locally within each agent

D To prevent agents from interacting with each other

💡 Hint

Think about how agents coordinate and share information.

Agent communication protocols define rules so agents can exchange messages clearly and coordinate actions.

❓ Model Choice

intermediate

Which protocol is best suited for negotiation between agents?

You want agents to negotiate offers and counteroffers to reach an agreement. Which communication protocol fits best?

A Contract Net Protocol

B Publish-Subscribe Protocol

C Request-Response Protocol

D Heartbeat Protocol

💡 Hint

Consider protocols designed for task allocation and bidding.

The Contract Net Protocol allows agents to announce tasks and negotiate bids, ideal for negotiation scenarios.

❓ Predict Output

advanced

What is the output of this agent message parsing code?

Given the code below that parses a message in a simple agent protocol, what will be printed?

Agentic AI

message = 'REQUEST:fetch_data'
command, content = message.split(':')
if command == 'REQUEST':
    print(f"Agent received request to {content}")
else:
    print("Unknown command")

A Unknown command

B Agent received request to REQUEST

C Agent received request to fetch_data

D fetch_data

💡 Hint

Look at how the message string is split and used.

The message splits into 'REQUEST' and 'fetch_data'. Since command is 'REQUEST', it prints the formatted string with content.

❓ Metrics

advanced

Which metric best measures communication efficiency in multi-agent protocols?

You want to evaluate how efficiently agents communicate in a system. Which metric is most appropriate?

A Agent computation time per task

B Message overhead ratio (number of messages sent vs. useful information exchanged)

C Memory usage of each agent

D Number of agents in the system

💡 Hint

Think about measuring communication cost versus benefit.

Message overhead ratio shows how many messages are needed to exchange useful info, indicating communication efficiency.

🔧 Debug

expert

Why does this agent communication code cause a deadlock?

Two agents use the following code to send and wait for messages. Why does this cause a deadlock? Agent A: response = agent_b.send('REQUEST') result = response.wait() Agent B: request = agent_a.receive() agent_a.send('RESPONSE') Options:

A Agent B tries to send a response before receiving the request, causing a deadlock

B Agent B receives the request but never sends a response, so Agent A waits forever

C Agent A sends a request but never waits for a response, so Agent B waits forever

D Agent A waits for a response before Agent B has received the request, causing both to wait indefinitely

💡 Hint

Consider the order of send and wait calls and how agents block.

Agent A sends a request and immediately waits for a response. Agent B waits to receive before sending. If Agent A's wait blocks Agent B's receive, both wait forever causing deadlock.