LEVEL
Practical

DURATION
8 hours

TRAINER
CIM4.0

PRICE

Description

Developing Autonomous Agents: from theory to practice

This intensive 8-hour course is designed for developers, software engineers, and technicians who wish to gain practical skills in developing applications based on Crew AI and autonomous agents. Through a hands-on approach, participants will learn how 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 the development of a complete AI application and providing tools for debugging, monitoring, and scaling solutions.

Macro Topics

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

  • Setting goals and tasks
  • Setting up an agent with Crew AI
  • Integration with LLM via API

• Practical Exercise: Creating a Basic AI Agent

  • Installation and setup of the development environment
  • Creating an agent that executes tasks independently

Module 3: Automation and AI Agent Interaction.

• 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 collaboration between agents

Module 4: Optimization and Debugging of AI Agents.

• Improving the Efficiency of AI Agents

  • Strategies for optimizing performance
  • Reducing computational costs and resource management
  • Prevention of endless loops and common errors

• Practical Exercise: Debugging and Optimization of AI Agents

  • Identification and resolution of common problems
  • Implementation of logging and monitoring mechanisms

Module 5: Deploy and Integration into Enterprise Environments.

• Deployment of a Multi-Agent AI Application.

  • Deployment options: cloud, on-premises, API
  • Security and access management
  • Strategies for continuous improvement

Trainee’s Profile

The course is aimed at developers, software engineers and technical

Pre-Requirements

• Basic experience in Python programming
• Knowledge of API and databases (optional, but recommended)
• Familiarity with the concept of language models (LLM)

Objectives

Upon completion of the course, participants will be able to understand the operation and potential of Crew AI for creating autonomous agents. Implement a multi-agent AI system with task management and orchestration. Write code for the creation, interaction and automation of autonomous agents.

More info

English language available upon request.
The subscription must be carried out within the fifth working day preceding the course starting date.