Visão Geral

Este curso ensina, na prática, como criar agentes inteligentes, assistentes personalizados e automações completas usando modelos GPT modernos. A ideia é unir estrutura tradicional de desenvolvimento com criatividade e ferramentas atuais, permitindo que você construa soluções realmente úteis para empresas, produtos digitais e rotinas de trabalho.

Objetivo

Após realizar este curso, você será capaz de:

• Criar assistentes inteligentes totalmente funcionais
• Construir agentes com funções, memória e RAG
• Automatizar processos de negócios
• Integrar GPTs com APIs externas
• Criar fluxos multi-etapas e multi-agentes
• Desenvolver soluções robustas para empresas e produtos

Publico Alvo

• Desenvolvedores iniciantes e avançados
• Profissionais de automação
• Empreendedores digitais
• Analistas de negócios
• Times de suporte e atendimento
• Criadores de produtos com IA
• Qualquer pessoa que deseja dominar agentes inteligentes

Pre-Requisitos

• Conhecimentos básicos de lógica de programação
• Noções de APIs
• Conta ativa na OpenAI
• Conhecimento básico em JavaScript ou Python

Conteúdo Programatico

Module 1 — Foundations of AI Assistants and Agents

1. Difference between chatbots, assistants and agents
2. Components of an intelligent system
3. Capabilities of GPT models in automation

Module 2 — Prompt Engineering for Agents

1. Structure of system prompts
2. Role definition and behavioral consistency
3. Task decomposition and step-based reasoning

Module 3 — Building Your First Assistant

1. Chat architecture overview
2. Context handling
3. Deterministic and hybrid-response strategies

Module 4 — Function Calling Essentials

1. Defining tools
2. Creating schemas
3. Step-by-step execution flows
4. Real-world use cases

Module 5 — Multi-Agent Systems

1. When and why to use multiple agents
2. Agent collaboration strategies
3. Supervisors, planners and specialist agents
4. Practical coordination patterns

Module 6 — Memory Systems

1. Short-term vs. long-term memory
2. Vector-based memory
3. Conversation state and retrieval strategies
4. Best practices and pitfalls

Module 7 — RAG (Retrieval-Augmented Generation)

1. Document ingestion
2. Vector store creation
3. Retrieval workflows
4. Evaluation and optimization

Module 8 — Vision, Audio and Multimodality

1. Using images as inputs
2. Extracting structured data from screenshots
3. Speech-to-text and text-to-speech with agents
4. Image generation inside workflows

Module 9 — Automating Workflows

1. Task scheduling
2. Multi-step automations
3. Integrating external services and APIs
4. Business process automation

Module 10 — Building Complete Agent Applications

1. Web, CLI and backend integration patterns
2. State management
3. Error handling and retry logic
4. Logging and monitoring

Module 11 — Scaling, Safety and Deployments

1. Rate limits and performance optimization
2. Safe output constraints
3. Privacy and data governance
4. Deploying agents to production

Module 12 — Final Project: Intelligent Automation System

Create a complete agent solution, such as:

1. Customer support assistant
2. AI task automation bot
3. Multi-agent productivity suite
4. RAG-powered knowledge assistant
5. Business workflow orchestrator

Includes:

1. Planning
2. Development
3. Testing
4. Deployment