AutoGen for conversational agents in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple conversational agent that replies with a greeting.

Agentic AI

agent = AutoGenAgent(name='ChatBot')
response = agent.[1]('Hello!')

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

Areply

Btalk

Csend

Drespond

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'respond' instead of 'reply' causes an AttributeError.

Using 'send' or 'talk' are not valid methods for this agent.

The method to get a reply from the agent is 'reply'.

2fill in blank

medium

Complete the code to initialize the agent with a system message that sets its role.

Agentic AI

agent = AutoGenAgent(name='HelperBot', system_message=[1])

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

A'You are a helpful assistant.'

B'Hello, I am your friend.'

C'Goodbye and take care.'

D'Random chat agent.'

Attempts:

3 left

💡 Hint

Common Mistakes

Using greetings or farewells as system messages confuses the agent's role.

Using vague descriptions does not set a clear role.

The system message defines the agent's role; a helpful assistant is the correct role here.

3fill in blank

hard

Fix the error in the code to correctly generate a conversation turn with the agent.

Agentic AI

conversation = Conversation()
conversation.add_user_message('Hi')
response = conversation.[1]()

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

Asend_message

Bstart_conversation

Cget_reply

Dgenerate_response

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'get_reply' causes an AttributeError.

Using 'send_message' or 'start_conversation' are incorrect method calls.

The method to generate the agent's reply in the conversation is 'generate_response'.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps user messages to their lengths if length is greater than 5.

Agentic AI

lengths = {msg: [1] for msg in messages if len(msg) [2] 5}

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

Alen(msg)

B>

C<

Dmsg

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'msg' as value instead of 'len(msg)' gives the message text, not length.

Using '<' instead of '>' filters the wrong messages.

We want the length of each message as the value, and filter messages longer than 5 characters.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of agent replies longer than 10 characters.

Agentic AI

filtered_replies = [1]: [2] for [3], reply in replies.items() if len(reply) > 10}

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

Auser

Breply

Dagent

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'agent' as key or variable name causes confusion.

Mixing up keys and values in the comprehension.

The dictionary keys are users, values are replies, iterating over user, reply pairs.

Practice

1. What is the main purpose of AutoGen in building conversational agents?

easy

A. To create multiple agents that can talk and work together

B. To train a single agent using large datasets

C. To generate images from text prompts

D. To analyze sentiment in user messages

Solution

1. Step 1: Understand AutoGen's role

   AutoGen is designed to help build chat helpers that can talk and cooperate with each other.

2. Step 2: Compare options to AutoGen's purpose

   Only To create multiple agents that can talk and work together matches this by describing multiple agents talking and working together.

3. Final Answer:

   To create multiple agents that can talk and work together -> Option A

4. Quick Check:

   AutoGen = multi-agent chat helpers [OK]

Hint: AutoGen means multiple agents chatting and cooperating [OK]

Common Mistakes:

• Thinking AutoGen trains a single agent only
• Confusing AutoGen with image generation tools
• Assuming AutoGen analyzes sentiment alone

2. Which of the following is the correct way to define a User agent in AutoGen?

easy

A. User = AutoAgent(name='User')

B. User = Agent(name='User')

C. User = AutoGenAgent('User')

D. User = AgenticAI(name='User')

Solution

1. Step 1: Recall AutoGen agent creation syntax

   AutoGen uses AutoAgent(name='AgentName') to create agents.

2. Step 2: Match options with correct syntax

   Only User = AutoAgent(name='User') uses AutoAgent with the correct parameter name='User'.

3. Final Answer:

   User = AutoAgent(name='User') -> Option A

4. Quick Check:

   Agent creation uses AutoAgent(name=...) [OK]

Hint: AutoGen agents use AutoAgent(name='...') syntax [OK]

Common Mistakes:

• Using wrong class names like Agent or AgenticAI
• Missing the name parameter or using positional args
• Confusing AutoGen with other AI libraries

3. Given this code snippet, what will be the output of print(conversation.history)?

user = AutoAgent(name='User')
assistant = AutoAgent(name='Assistant')
conversation = AutoConversation(agents=[user, assistant])
conversation.start()
conversation.step()
print(conversation.history)

medium

A. A dictionary with agent names as keys and messages as values

B. A list containing the User's and Assistant's messages in order

C. An empty list because no messages were exchanged

D. A string with concatenated messages from both agents

Solution

1. Step 1: Understand conversation start and step

   conversation.start() initializes the conversation, and conversation.step() runs one exchange between agents.

2. Step 2: Check what conversation.history stores

   It stores a list of messages exchanged in order, from User and Assistant.

3. Final Answer:

   A list containing the User's and Assistant's messages in order -> Option B

4. Quick Check:

   conversation.history = list of messages [OK]

Hint: conversation.history holds ordered message list [OK]

Common Mistakes:

• Thinking history is empty after one step
• Expecting a dictionary instead of a list
• Assuming history is a single string

4. Identify the error in this AutoGen code snippet:

user = AutoAgent(name='User')
assistant = AutoAgent(name='Assistant')
conversation = AutoConversation(agents=[user, assistant])
conversation.start()
conversation.step()
print(conversation.history)
conversation.step()

medium

A. Not importing AutoAgent and AutoConversation modules

B. Missing agent names in AutoAgent initialization

C. Using print() instead of return to get history

D. Calling conversation.step() twice without checking if conversation ended

Solution

1. Step 1: Review conversation step usage

   Calling conversation.step() advances the conversation. Calling it twice without checking if conversation ended can cause errors.

2. Step 2: Check other code parts

   Agent names are provided, print() is valid for output, and imports are assumed correct.

3. Final Answer:

   Calling conversation.step() twice without checking if conversation ended -> Option D

4. Quick Check:

   Multiple steps need end check [OK]

Hint: Check if conversation ended before calling step again [OK]

Common Mistakes:

• Ignoring conversation end status before stepping
• Assuming print() is invalid for output
• Forgetting to import but not shown here

5. You want to build a multi-agent chatbot where User, Assistant, and Moderator agents interact. Which approach best uses AutoGen to achieve this?

hard

A. Create agents using different libraries and merge their outputs manually

B. Train a single AutoAgent with combined roles of User, Assistant, and Moderator

C. Create three AutoAgent instances for User, Assistant, and Moderator, then run AutoConversation with all agents

D. Use AutoGen to generate separate conversations for each agent independently

Solution

1. Step 1: Understand multi-agent setup in AutoGen

   AutoGen supports multiple agents interacting by creating separate AutoAgent instances for each role.

2. Step 2: Choose the approach that runs all agents together

   Running AutoConversation with all agents allows them to talk and cooperate in one chat.

3. Final Answer:

   Create three AutoAgent instances for User, Assistant, and Moderator, then run AutoConversation with all agents -> Option C

4. Quick Check:

   Multi-agent chat = multiple AutoAgent + one AutoConversation [OK]

Hint: Use one AutoAgent per role, run all in AutoConversation [OK]

Common Mistakes:

• Trying to combine roles into one agent
• Running agents separately without conversation
• Mixing different libraries causing integration issues