Padrões de colaboração entre múltiplos agentes
Explore estratégias para projetar sistemas nos quais vários agentes de IA trabalhem juntos para resolver um problema maior.
Padrões de colaboração entre múltiplos agentes é uma aula grátis de AI Agents with LangChain & Autonomous Workflows no CoddyKit. Esta é a aula 5 de 6. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de AI Agents with LangChain & Autonomous Workflows, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de AI Agents with LangChain & Autonomous Workflows inclui 6 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
Working Together: Multi-Agent Systems
Welcome to Multi-Agent Collaboration Patterns! In this lesson, we'll explore how multiple AI agents can work together to solve complex problems.
Think of it like a team: sometimes there's a manager, sometimes everyone brainstorms, and sometimes there's a shared whiteboard. AI agents can use similar strategies!
The Power of Agent Collaboration
Why make agents collaborate? Just like human teams, multiple agents can achieve more than one working alone. Here are key benefits:
- Tackle Complexity: Break down large problems into smaller, manageable tasks.
- Speed & Efficiency: Agents can work in parallel, speeding up overall execution.
- Robustness: If one agent fails, others might pick up the slack or provide alternative solutions.
- Diverse Perspectives: Different agents can specialize in different skills or knowledge areas.
Core Multi-Agent Collaboration Patterns
When designing systems with multiple agents, we often use established collaboration patterns. These patterns define how agents interact, share information, and coordinate their actions.
We'll look at three common ones:
- Hierarchical Collaboration
- Peer-to-Peer Collaboration
- Blackboard Architecture
Hierarchical: Manager & Workers
In a Hierarchical Collaboration pattern, there's a clear leader-follower structure. One agent, often called the 'manager' or 'orchestrator', delegates tasks to other 'worker' agents.
The manager oversees the overall goal, breaks it down, assigns parts, and synthesizes the results from the workers. This is great for structured problems.
Hierarchical Agent Example
Here's a simple Python example of a hierarchical setup. A project_manager_agent orchestrates a researcher_agent and a writer_agent to complete a task.
def researcher_agent(topic):
print(f" Researcher: Searching for data on '{topic}'...")
return f"Data found for {topic}."
def writer_agent(data):
print(f" Writer: Drafting report using '{data}'...")
return f"Report drafted from {data}."
def project_manager_agent(project_name):
print(f"Manager: Starting project '{project_name}'.")
research_result = researcher_agent(project_name)
final_report = writer_agent(research_result)
print(f"Manager: Project '{project_name}' complete. Final output: {final_report}")
if __name__ == "__main__":
project_manager_agent("AI Agent Architectures")Peer-to-Peer: Agents as Equals
In Peer-to-Peer (P2P) Collaboration, agents interact directly with each other without a central coordinator. All agents are considered equals, and they communicate to share information, negotiate, or brainstorm.
This pattern is often used for problems where tasks are less structured, and agents need more autonomy or collective decision-making.
Peer-to-Peer Agent Discussion
This Python snippet simulates a simple peer-to-peer discussion where agents build on each other's comments. There's no single manager; each agent contributes directly.
def discuss_topic(topic, agent_name, previous_comment=None):
if previous_comment:
print(f"{agent_name}: Building on '{previous_comment}', I think {topic} is complex.")
return f"{agent_name} notes complexity."
else:
print(f"{agent_name}: Let's start discussing '{topic}'.")
return f"{agent_name} initiates discussion."
if __name__ == "__main__":
topic = "Future of AI"
comment1 = discuss_topic(topic, "Agent Alpha")
comment2 = discuss_topic(topic, "Agent Beta", comment1)
comment3 = discuss_topic(topic, "Agent Gamma", comment2)
print("\nDiscussion complete for now.")Blackboard: Shared Information Hub
The Blackboard Architecture uses a shared data repository, the 'blackboard', where agents can post problems, partial solutions, or new information. Agents monitor the blackboard and contribute when they have relevant expertise.
This is highly flexible and suited for ill-defined problems where different agents might contribute at different times, asynchronously, to a common goal.
Blackboard Agent System
Here's a Python example demonstrating a blackboard system. Agents post and retrieve information from a shared Blackboard object to solve a problem.
class Blackboard:
def __init__(self):
self.knowledge = []
def post(self, data):
self.knowledge.append(data)
print(f"Blackboard: Posted '{data}'")
def get_relevant(self, keyword):
return [item for item in self.knowledge if keyword in item]
def agent_analyzer(blackboard_ref):
data = blackboard_ref.get_relevant("problem")
if data:
solution = f"Solution for {data[0]}"
blackboard_ref.post(solution)
print(f" Analyzer Agent: Posted '{solution}'")
def agent_reporter(blackboard_ref):
solutions = blackboard_ref.get_relevant("Solution")
if solutions:
print(f" Reporter Agent: Found solutions: {', '.join(solutions)}")
if __name__ == "__main__":
shared_blackboard = Blackboard()
print("--- Initializing Blackboard System ---")
shared_blackboard.post("Initial problem: High CPU usage")
agent_analyzer(shared_blackboard)
shared_blackboard.post("New finding: Memory leak detected (problem)")
agent_analyzer(shared_blackboard)
agent_reporter(shared_blackboard)
print("--- Blackboard System End ---")Test Your Collaboration Knowledge
Which of the following statements are true about multi-agent collaboration patterns?
Multi-Agent Systems: A Powerful Future
Congratulations! You've explored the fascinating world of multi-agent collaboration.
We covered:
- The benefits of agents working together.
- Hierarchical patterns for structured tasks.
- Peer-to-Peer patterns for autonomous interaction.
- Blackboard architectures for flexible, shared problem-solving.
These patterns are crucial for building sophisticated AI systems that can tackle real-world challenges more effectively. Keep exploring how you can apply them in your own agent designs!
Perguntas Frequentes
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O que vou aprender em “Padrões de colaboração entre múltiplos agentes”?
Explore estratégias para projetar sistemas nos quais vários agentes de IA trabalhem juntos para resolver um problema maior. Você pratica AI Agents with LangChain & Autonomous Workflows com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.
Preciso ter experiência prévia para começar AI Agents with LangChain & Autonomous Workflows?
Nenhuma experiência prévia é necessária. AI Agents with LangChain & Autonomous Workflows no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 5 de 6.
Quanto tempo leva a aula “Padrões de colaboração entre múltiplos agentes”?
A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.
Posso escrever e executar código nesta aula de AI Agents with LangChain & Autonomous Workflows?
Sim. Cada aula de AI Agents with LangChain & Autonomous Workflows inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.
Todas as aulas deste curso
- Agentes ReAct e de planejamento e execução
- Designs hierárquicos de agentes
- Agentes de autocorreção e reflexão
- Arquiteturas cognitivas para agentes
- Padrões de colaboração entre múltiplos agentes
- Sistemas híbridos de agentes