Introduction to Advanced Agent Types

As we delve deeper into the world of generative AI and agentic systems, it's crucial to understand the more sophisticated agent types available in Microsoft's AutoGen framework. Today, we'll focus on two particularly powerful agents: GroupChatManager and TeachableAgent.

GroupChatManager: Orchestrating Multi-Agent Conversations

The GroupChatManager is a specialized agent designed to facilitate and manage conversations between multiple agents. Think of it as a digital moderator or facilitator for AI discussions.

Key Features:

1. Conversation Control: The GroupChatManager initiates, directs, and concludes multi-agent conversations.
2. Dynamic Participant Management: It can add or remove agents from the conversation as needed.
3. Context Awareness: Maintains and updates the conversation context for all participants.

Example Use Case:

Imagine you're developing a complex project management AI system. You could use a GroupChatManager to coordinate discussions between specialized agents like:

• A planning agent
• A resource allocation agent
• A risk assessment agent
• A timeline management agent

The GroupChatManager would ensure these agents collaborate effectively, sharing insights and making collective decisions.

from autogen import GroupChatManager, Agent

planner = Agent("Planner")
resource_manager = Agent("Resource Manager")
risk_assessor = Agent("Risk Assessor")
timeline_manager = Agent("Timeline Manager")

group_chat = GroupChatManager(
    agents=[planner, resource_manager, risk_assessor, timeline_manager],
    manager_prompt="Coordinate the project planning discussion."
)

group_chat.initiate_chat("Let's develop a plan for our new software project.")

TeachableAgent: Adaptive Learning in Action

The TeachableAgent is a fascinating concept in AutoGen. It's an agent that can learn and adapt its behavior based on feedback and new information.

Key Features:

1. Dynamic Knowledge Base: The agent can update its knowledge in real-time.
2. Feedback Integration: It processes and incorporates user feedback to improve its responses.
3. Continuous Learning: The TeachableAgent evolves with each interaction, becoming more accurate and helpful over time.

Example Use Case:

Let's say you're creating a customer support AI. A TeachableAgent could start with basic product knowledge and learn from interactions with customers and support staff.

from autogen import TeachableAgent

support_agent = TeachableAgent(
    name="Support AI",
    initial_knowledge="Basic product manual and FAQs"
)

# Simulating an interaction
customer_query = "How do I reset my password?"
response = support_agent.respond(customer_query)

# Feedback loop
human_feedback = "Good response, but also mention two-factor authentication."
support_agent.learn(human_feedback)

# The agent will now incorporate information about two-factor authentication in future responses about password resets.

Combining GroupChatManager and TeachableAgent

The real power of these advanced agent types shines when you combine them. Imagine a group chat of TeachableAgents, managed by a GroupChatManager. This setup could lead to a highly adaptive, collaborative AI system that learns and improves collectively.

teachable_agent1 = TeachableAgent("Expert 1")
teachable_agent2 = TeachableAgent("Expert 2")
teachable_agent3 = TeachableAgent("Expert 3")

adaptive_group_chat = GroupChatManager(
    agents=[teachable_agent1, teachable_agent2, teachable_agent3],
    manager_prompt="Coordinate the discussion and ensure collective learning."
)

adaptive_group_chat.initiate_chat("Let's solve this complex problem together and learn from each other.")

In this scenario, each agent not only contributes its expertise but also learns from the others, creating a synergistic learning environment.

Practical Tips for Working with Advanced Agents

1. Clear Objectives: Define clear goals for your GroupChatManager to guide the conversation effectively.
2. Quality Feedback: Provide specific, actionable feedback to TeachableAgents for optimal learning.
3. Balanced Participation: In group chats, ensure all agents have opportunities to contribute and learn.
4. Regular Evaluation: Periodically assess the performance of your advanced agents and adjust as needed.

By leveraging these advanced agent types in AutoGen, you can create more dynamic, adaptive, and collaborative AI systems. Whether you're building a complex problem-solving application or an evolving customer support platform, GroupChatManager and TeachableAgent offer powerful tools to enhance your agentic AI projects.