AutoGen for conversational agents in Agentic AI - Model Metrics & Evaluation

Metrics & Evaluation - AutoGen for conversational agents

Which metric matters for AutoGen conversational agents and WHY

For AutoGen conversational agents, key metrics include accuracy for intent recognition, precision and recall for entity extraction, and F1 score to balance both. These metrics matter because the agent must correctly understand user requests (high recall) and avoid false triggers (high precision) to respond helpfully and naturally.

Confusion matrix example for intent classification

       Predicted
       |  Yes  |  No  
    ---+-------+-------
    Yes|  80   |  20   
    No |  10   |  90   
    
    TP = 80 (correctly predicted 'Yes')
    FP = 10 (wrongly predicted 'Yes')
    FN = 20 (missed 'Yes')
    TN = 90 (correctly predicted 'No')
    

From this, precision = 80 / (80 + 10) = 0.89, recall = 80 / (80 + 20) = 0.80.

Precision vs Recall tradeoff with examples

Imagine the agent detects when a user wants to book a flight. If precision is high but recall is low, the agent rarely makes mistakes but misses many booking requests, frustrating users. If recall is high but precision is low, the agent tries to book flights too often, annoying users with wrong actions. Balancing precision and recall with F1 score helps the agent respond accurately and reliably.

What good vs bad metric values look like for AutoGen conversational agents

• Good: Precision and recall above 0.85, F1 score above 0.85, showing balanced and reliable understanding.
• Bad: Precision or recall below 0.5, indicating many false alarms or missed intents, leading to poor user experience.

Common pitfalls in metrics for conversational agents

• Accuracy paradox: High accuracy can be misleading if one intent dominates the data.
• Data leakage: Training on future conversation turns can inflate metrics falsely.
• Overfitting: Very high training metrics but poor real user performance.

Self-check question

Your conversational agent has 98% accuracy but only 12% recall on booking requests. Is it good for production? Why or why not?

Answer: No, it is not good. The low recall means the agent misses most booking requests, failing to help users even though overall accuracy looks high.

Key Result

Balanced precision and recall are key to reliable conversational agents; high accuracy alone can be misleading.

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