Introduction to Custom Agent Behaviors in AutoGen

Microsoft's AutoGen framework has opened up exciting possibilities for creating intelligent, task-oriented AI agents. While the framework provides a solid foundation, implementing custom behaviors and response patterns allows developers to create truly unique and powerful agents tailored to specific use cases.

In this blog post, we'll explore how to elevate your AutoGen agents by implementing custom behaviors and fine-tuning their response patterns. Let's dive in!

Understanding Agent Behavior Components

Before we start customizing, it's essential to understand the key components that make up an agent's behavior:

1. Task understanding
2. Decision-making process
3. Response generation
4. Memory and context management
5. Inter-agent communication

By focusing on these areas, we can create more sophisticated and effective agents.

Implementing Custom Task Understanding

To improve an agent's ability to understand tasks, we can enhance its natural language processing capabilities. Here's an example of how to implement a custom task parser:

from autogen import Agent
import spacy

class CustomTaskAgent(Agent):
    def __init__(self, name):
        super().__init__(name)
        self.nlp = spacy.load("en_core_web_sm")

    def parse_task(self, task_description):
        doc = self.nlp(task_description)
        
        # Extract key information from the task description
        action = [token.lemma_ for token in doc if token.pos_ == "VERB"][0]
        objects = [chunk.text for chunk in doc.noun_chunks]
        
        return {"action": action, "objects": objects}

# Usage
agent = CustomTaskAgent("TaskMaster")
task = "Summarize the latest tech news articles"
parsed_task = agent.parse_task(task)
print(parsed_task)

This custom task parser uses the spaCy library to extract key information from the task description, allowing the agent to better understand and act on the given instructions.

Enhancing Decision-Making Processes

To improve an agent's decision-making capabilities, we can implement more advanced algorithms or integrate external knowledge bases. Here's an example of a custom decision-making process that uses a simple scoring system:

import random

class DecisionMakingAgent(Agent):
    def __init__(self, name):
        super().__init__(name)
        self.knowledge_base = {
            "summarize": 0.8,
            "analyze": 0.7,
            "research": 0.6,
            "write": 0.5
        }

    def make_decision(self, parsed_task):
        action = parsed_task["action"]
        confidence = self.knowledge_base.get(action, 0.3)
        
        if confidence > 0.6:
            return f"I'll {action} with high confidence"
        elif confidence > 0.4:
            return f"I can try to {action}, but I'm not entirely sure"
        else:
            return "I don't think I can perform this task effectively"

# Usage
agent = DecisionMakingAgent("Decider")
task = {"action": "summarize", "objects": ["tech news articles"]}
decision = agent.make_decision(task)
print(decision)

This example demonstrates a simple decision-making process based on the agent's predefined knowledge and confidence levels for different actions.

Customizing Response Generation

To create more natural and contextually appropriate responses, we can implement custom response generation methods. Here's an example that uses templates and context-aware filling:

import random

class ResponseAgent(Agent):
    def __init__(self, name):
        super().__init__(name)
        self.templates = {
            "greeting": ["Hello!", "Hi there!", "Greetings!"],
            "task_accepted": ["I'd be happy to {task}.", "Certainly! I'll {task} right away.", "No problem, I'll get started on {task}."],
            "task_completed": ["I've finished {task}.", "The {task} is done.", "I've completed {task} as requested."]
        }

    def generate_response(self, response_type, context=None):
        template = random.choice(self.templates[response_type])
        if context:
            return template.format(**context)
        return template

# Usage
agent = ResponseAgent("Responder")
print(agent.generate_response("greeting"))
print(agent.generate_response("task_accepted", {"task": "summarizing the articles"}))
print(agent.generate_response("task_completed", {"task": "summary"}))

This custom response generator uses predefined templates and context-aware filling to create more varied and natural-sounding responses.

Improving Memory and Context Management

Effective agents need to maintain context and remember important information. Here's an example of a simple memory management system:

class MemoryAgent(Agent):
    def __init__(self, name):
        super().__init__(name)
        self.short_term_memory = []
        self.long_term_memory = {}

    def remember(self, information, importance):
        self.short_term_memory.append(information)
        if len(self.short_term_memory) > 5:
            self.short_term_memory.pop(0)
        
        if importance > 0.7:
            key = hash(information)
            self.long_term_memory[key] = information

    def recall(self, query):
        # Check short-term memory
        for info in reversed(self.short_term_memory):
            if query in info:
                return info
        
        # Check long-term memory
        for info in self.long_term_memory.values():
            if query in info:
                return info
        
        return "I don't recall any information related to that query."

# Usage
agent = MemoryAgent("Memorizer")
agent.remember("The capital of France is Paris", 0.8)
agent.remember("The sky is blue", 0.5)
print(agent.recall("capital"))
print(agent.recall("color"))

This memory system demonstrates how an agent can maintain both short-term and long-term memory, allowing it to recall information based on importance and recency.

Enhancing Inter-Agent Communication

For more complex systems involving multiple agents, implementing custom communication protocols can greatly improve collaboration. Here's an example of a simple message passing system:

class CommunicativeAgent(Agent):
    def __init__(self, name):
        super().__init__(name)
        self.inbox = []

    def send_message(self, recipient, message):
        recipient.receive_message(self.name, message)

    def receive_message(self, sender, message):
        self.inbox.append({"sender": sender, "message": message})

    def process_messages(self):
        for message in self.inbox:
            print(f"Processing message from {message['sender']}: {message['message']}")
        self.inbox = []

# Usage
agent1 = CommunicativeAgent("Agent1")
agent2 = CommunicativeAgent("Agent2")

agent1.send_message(agent2, "Hello, can you help me with a task?")
agent2.send_message(agent1, "Sure, what do you need?")

agent1.process_messages()
agent2.process_messages()

This example shows how agents can send and receive messages, enabling more complex interactions and task delegation between multiple agents.

Conclusion

By implementing these custom behaviors and response patterns, you can create more sophisticated and effective agents using the Microsoft AutoGen framework. These enhancements allow your agents to better understand tasks, make informed decisions, generate natural responses, maintain context, and collaborate with other agents.

As you continue to explore and experiment with custom agent behaviors, you'll discover new ways to tailor your AI agents to specific use cases and requirements, unlocking the full potential of generative AI in your projects.