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Artificial Intelligence

AI Agents: Development of Autonomous Agents from Theory to Practice

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Course description

The course is aimed at developers, software engineers, and technicians who wish 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 scalability of solutions.

Main 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
  • Key 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 Goals and Tasks
    • Configuring 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 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
    • Configuring Multiple Specialized Agents
    • Implementing Collaboration Between Agents

Module 4: Optimization and Debugging of AI Agents

  • Improving AI Agents’ Efficiency
    • Strategies to Optimize Performance
    • Reducing Computational Costs and Resource Management
    • Preventing Infinite Loops and Common Errors
  • Practical Exercise: Debugging and Optimization of AI Agents
    • Identifying and Resolving Common Issues
    • Implementing Logging and Monitoring Mechanisms

Module 5: Deployment and Integration in Business Environments

  • Deployment of a Multi-Agent AI Application
    • Deployment Options: Cloud, On-Premises, API
    • Security and Access Management
    • Strategies for Continuous Improvement

Participant profile

The course is aimed at developers, software engineers, and technicians

Objectives

At the end of the course, participants will be able to:

  • Understand the functionality 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.

Learning outcomes

At the end of the course, participants will be able to:

  • Describe the fundamental concepts of autonomous agents and multi-agent AI architectures, recognizing their differences and advantages over traditional artificial intelligence.
  • Use Crew AI to design and configure autonomous agents, defining goals, tasks, and integrating large language models (LLM) via API.
  • Develop AI applications with multi-agent architectures, implementing communication, coordination, and task division among specialized agents.
  • Optimize the performance and behavior of AI agents, applying debugging, logging, and monitoring techniques to improve operational efficiency and prevent common errors.
  • Practically apply the skills acquired through hands-on exercises, developing and testing autonomous AI solutions ready for real-world and business scenarios.

Participation conditions

  • Basic experience in Python programming
  • Knowledge of APIs and databases (optional but recommended)
  • Familiarity with the concept of Large Language Models (LLM)

Cost per participant

€300

Participants

Min 5

Resources available from PNRR funds, Mission 4 – Component 2 – Investment 2.3:

MICRO-SMALL ENTERPRISES: 80%
MEDIUM ENTERPRISES: 70%
LARGE ENTERPRISES: 40%

Cost per company

€1500

Participants

Max 15 per company

Mode

In-person

Duration

12 hours

Level

Practical

Trainer

CIM

At the end of each course, you will receive a certificate of participation issued by the CIM4.0 Competence Center.
As an implementing body for digital transition, CIM4.0 holds the accreditation certificate from the Piedmont Region for the provision of continuous training. Certificate No.: 1503/001

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