AutoGen for conversational agents in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding AutoGen's role in conversational agents

What is the primary purpose of AutoGen in building conversational agents?

A To replace human agents entirely without any training data

B To automatically generate dialogue flows and manage multi-turn conversations

C To manually code every possible user input and response

D To only analyze sentiment in user messages without generating replies

💡 Hint

Think about what 'AutoGen' implies about conversation creation.

AutoGen helps create and manage dialogue automatically, enabling smooth multi-turn conversations without manual scripting.

❓ Model Choice

intermediate

Choosing the right model for AutoGen conversational agents

Which type of model is best suited for AutoGen to generate context-aware responses in a conversational agent?

A A decision tree trained on static FAQs

B A simple linear regression model

C A clustering algorithm for grouping user intents

D A large language model fine-tuned on dialogue datasets

💡 Hint

Consider which model can generate natural language text based on context.

Large language models fine-tuned on dialogue data can generate fluent, context-aware responses, which is essential for AutoGen conversational agents.

❓ Predict Output

advanced

Output of AutoGen conversation snippet

What is the output of this AutoGen conversation code snippet?

Agentic AI

conversation = AutoGenConversation()
conversation.add_user_message('Hello, can you help me book a flight?')
response = conversation.generate_response()
print(response)

A "I don't understand your request."

B "Error: conversation object has no attribute 'add_user_message'"

C "Sure! Where would you like to fly to?"

D "Booking confirmed for your flight."

💡 Hint

The first user message is a request; the agent should ask for more details.

The agent responds by asking for the destination to proceed with booking, showing context understanding.

❓ Hyperparameter

advanced

Tuning AutoGen response creativity

Which hyperparameter adjustment increases the creativity and diversity of responses generated by AutoGen conversational agents?

A Increasing the temperature value during text generation

B Decreasing the batch size during training

C Reducing the number of training epochs

D Setting the learning rate to zero

💡 Hint

Temperature controls randomness in generated text.

Higher temperature values make the model produce more diverse and creative responses by increasing randomness.

❓ Metrics

expert

Evaluating AutoGen conversational agent performance

Which metric best measures how well an AutoGen conversational agent maintains context over multiple turns?

A Contextual coherence score computed by comparing embeddings of consecutive turns

B Accuracy of single-turn intent classification

C BLEU score comparing generated responses to a fixed reference

D Mean squared error of predicted numerical outputs

💡 Hint

Think about measuring how responses relate to previous conversation parts.

Contextual coherence score evaluates how well the agent keeps the conversation relevant and connected across turns.