IA Agentiva
Corso avanzato sulla costruzione di agenti IA autonomi. Impara a creare flussi di lavoro intelligenti che possono operare indipendentemente e prendere decisioni.
Programma
Introduction to Agentic AI
Understanding the paradigm shift from passive chatbots to active agents. We define what makes an AI 'agentic': the ability to perceive, reason, plan, and act autonomously to achieve goals. We explore the core architecture of modern agents.
Core Components: Memory & Planning
Deep dive into the cognitive architecture of agents. Learn how to implement short-term and long-term memory (Vector DBs) and explore planning strategies like ReAct (Reasoning + Acting) and Chain of Thought to enable complex problem-solving.
Tool Use & Function Calling
Agents are only as powerful as the tools they wield. Master the art of defining tools and function schemas that allow LLMs to interact with the real world—APIs, databases, and software systems. We will build a custom toolset for a live agent.
Multi-Agent Frameworks
Introduction to multi-agent orchestration. Learn how to design systems where specialized agents (e.g., a Researcher, a Writer, and a Reviewer) collaborate to solve complex tasks. We will use frameworks like LangGraph or AutoGen.
Model Context Protocol (MCP)
Explore the new standard for connecting AI models to data and tools. Understand how MCP simplifies the integration of agents with local and remote resources, and build a simple MCP server.
Capstone Project: Autonomous Workflow
Apply everything you've learned to build a fully autonomous workflow. Participants will design and implement an agentic system that solves a specific business problem, from trigger to final output, demonstrating real-world value.
Chi Dovrebbe Partecipare
Software Developers, Data Scientists, Technical Architects, and AI Engineers who want to move beyond simple chatbots and RAG applications to build autonomous, decision-making AI systems.
La Nostra Piattaforma Interattiva
Sperimenta un ambiente di apprendimento all'avanguardia progettato per il massimo coinvolgimento e l'applicazione nel mondo reale
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Agent Sandbox Environment
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Multi-Agent Orchestration Visualizer
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Custom Tool Builder Interface
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MCP Server Integration Hub
Punti Chiave da Ricordare
- → Understand the architecture and lifecycle of autonomous AI agents.
- → Master tool definition and function calling to connect LLMs to external systems.
- → Design and orchestrate multi-agent systems for complex task delegation.
- → Implement the Model Context Protocol (MCP) for standardized integration.
- → Build and deploy a functional autonomous agent for a business use case.
Prezzo Personalizzato
Richiedi Preventivo
In base a dimensione team, luogo e formato
Formato di Consegna
In presenza, Online o Ibrido
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