The course is designed for developers, software engineers, and technicians who want to acquire practical skills in developing applications based on Crew AI and autonomous agents. Through a hands-on approach, participants will learn to design, implement, and optimize an autonomous AI system, leveraging Large Language Models (LLM) and multi-agent architectures for process automation.
The course combines theory and practice, guiding participants in developing a complete AI application and providing tools for debugging, monitoring, and scaling solutions.
CONTENT
Module 1: Introduction to Crew AI and Autonomous Agents
What are Autonomous Agents?
• Introduction to multi-agent AI architectures.
• Differences between traditional AI and autonomous agents.
• Practical applications in business and industry.
Main Components of Crew AI:
• Structure and architecture of a multi-agent AI system.
• Overview of available tools and frameworks.
• Typical development workflow.
Module 2: Building an Autonomous Agent with Crew AI
Designing an AI Agent:
• Defining objectives and tasks.
• Configuring an agent with Crew AI.
• Integrating with LLM through API.
Practical Exercise: Creating a Basic AI Agent:
• Installation and development environment setup.
• Creating an agent that performs tasks autonomously.
Module 3: Automation and Interaction between AI Agents
Structuring a Multi-Agent AI System with Crew AI:
• Communication and coordination between agents.
• Task division strategies.
• Implementation of autonomous AI workflows.
Practical Exercise: Creating a Team of AI Agents
• Configuration of multiple specialized agents.
• Implementation of agent collaboration.
Module 4: Optimization and Debugging of AI Agents
Improving AI Agent Efficiency:
• Strategies for performance optimization.
• Reducing computational costs and resource management.
• Prevention of infinite loops and common errors.
Practical Exercise: Debugging and Optimizing AI Agents
• Identification and resolution of common issues.
• Implementation of logging and monitoring mechanisms.
Module 5: Deployment and Integration in Enterprise Environments
Deploying a Multi-Agent AI Application:
• Deployment options: cloud, on-premises, API.
• Security and access management.
• Strategies for continuous improvement.