Visão Geral

Este curso aborda os fundamentos, arquiteturas e práticas de implementação de sistemas multiagentes (Multi-Agent Systems - MAS), nos quais múltiplos agentes de Inteligência Artificial colaboram, coordenam-se e comunicam-se para resolver problemas complexos. O participante aprenderá como projetar ecossistemas de agentes especializados, implementar mecanismos de coordenação, orquestração, colaboração e negociação, além de explorar arquiteturas modernas baseadas em Large Language Models (LLMs), Agentic AI e Autonomous Agents. O curso também aborda aspectos de segurança, governança, observabilidade e escalabilidade para ambientes corporativos.

Objetivo

Após realizar este curso, você será capaz de:

• Compreender os fundamentos dos sistemas multiagentes
• Projetar arquiteturas compostas por múltiplos agentes especializados
• Implementar mecanismos de colaboração, coordenação e comunicação
• Construir soluções corporativas baseadas em agentes distribuídos
• Aplicar controles de segurança, governança e observabilidade
• Desenvolver plataformas escaláveis para ecossistemas multiagentes

Publico Alvo

• Engenheiros de IA e Machine Learning
• Desenvolvedores de Software
• Arquitetos de Soluções
• Profissionais de Automação Inteligente
• Engenheiros de Platform Engineering e LLMOps
• Líderes técnicos envolvidos em projetos avançados de IA

Pre-Requisitos

• Conhecimentos equivalentes aos cursos AI Agents Fundamentals e Agentic AI
• Familiaridade com Large Language Models
• Conhecimentos básicos de arquitetura de software distribuída
• Experiência com automação e integração de sistemas é recomendada

Conteúdo Programatico

Module 1: Introduction to Multi-Agent Systems

1. Evolution of multi-agent systems
2. Fundamentals of distributed intelligence
3. Agent specialization concepts
4. Enterprise use cases
5. Benefits and challenges
6. Multi-agent ecosystem overview

Module 2: Foundations of Agent Architectures

1. Agent architecture fundamentals
2. Autonomous agent principles
3. Agent roles and responsibilities
4. Agent lifecycle management
5. Agent interaction models
6. Design principles for multi-agent systems

Module 3: Communication Between Agents

1. Agent communication fundamentals
2. Messaging architectures
3. Communication protocols
4. Information sharing strategies
5. Context propagation techniques
6. Communication optimization methods

Module 4: Coordination and Orchestration

1. Coordination mechanisms
2. Workflow orchestration models
3. Task allocation strategies
4. Collaborative execution patterns
5. Dynamic coordination approaches
6. Orchestration frameworks

Module 5: Planning and Collaborative Reasoning

1. Distributed planning concepts
2. Collaborative problem-solving techniques
3. Shared reasoning models
4. Goal decomposition strategies
5. Decision-making coordination
6. Conflict resolution mechanisms

Module 6: Multi-Agent Memory and Knowledge Management

1. Shared memory architectures
2. Distributed knowledge repositories
3. Agent-specific memory systems
4. Knowledge synchronization techniques
5. Context management strategies
6. Knowledge governance principles

Module 7: Multi-Agent Systems and RAG

1. Agentic RAG architectures
2. Distributed retrieval systems
3. Collaborative knowledge discovery
4. Graph-based knowledge retrieval
5. Knowledge-grounded execution
6. Enterprise knowledge applications

Module 8: Autonomous Multi-Agent Workflows

1. Workflow automation architectures
2. Dynamic task execution
3. Autonomous collaboration models
4. Adaptive workflow strategies
5. Failure recovery mechanisms
6. Enterprise automation scenarios

Module 9: Scalability and Distributed Systems

1. Distributed agent architectures
2. Horizontal scalability techniques
3. Resource allocation strategies
4. Performance optimization methods
5. High-availability architectures
6. Enterprise deployment considerations

Module 10: Security, Governance and Risk Management

1. Multi-agent security challenges
2. Access control models
3. Governance frameworks
4. Risk management methodologies
5. Compliance requirements
6. Responsible AI practices

Module 11: Observability and Performance Evaluation

1. Agent observability architectures
2. Performance monitoring techniques
3. Communication analysis
4. Workflow evaluation methodologies
5. Reliability assessment
6. Continuous optimization practices

Module 12: Multi-Agent Systems Workshop

1. Multi-agent architecture design
2. Agent communication implementation
3. Collaborative workflow development
4. Distributed knowledge integration
5. Governance and monitoring validation
6. Final multi-agent system project