Abstract
Recent advancements in Generative AI are accelerating the development of autonomous AI agents capable of performing complex tasks with minimal human intervention. Yet, as these agents grow in capability, new challenges arise around interoperability, coordination, and the safe integration of AI-driven behaviours into real-world systems. This talk explores how, using no-code design, it is possible to bridge the gap between powerful AI models and practical autonomous workflows, enabling teams to design, control, and monitor agentic systems without requiring deep engineering expertise.
We will examine emerging interoperability standards—such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication protocols—that are shaping a new ecosystem where autonomous agents can reliably collaborate, exchange context, and coordinate actions. Beyond theory, the session will demonstrate a concrete application of these technologies through an autonomous agentic system, showcasing how no-code orchestration empowers secure, scalable, and transparent AI-driven operations.
- Salvatore Cipolla
Engineering Ingegneria Informatica S.p.A.Salvatore Cipolla is a researcher and data scientist at Engineering Ingegneria Informatica S.p.A., where he has been conducting research in Artificial Intelligence for many years. His work spans machine learning, large-scale data analysis, and intelligent systems. He is currently part of a multidisciplinary team developing a Large Language Model from scratch, specifically designed to support and enhance the Italian digital ecosystem. In parallel, he is actively engaged in research on AI agents and their applications within industrial environments, with a focus on enabling autonomous, adaptive, and efficient solutions for real-world processes. His scientific interests include LLM development, agent-based AI architectures, and applied AI for enterprise innovation.
- Daniele Fakhoury
Engineering Ingegneria Informatica S.p.A.Daniele Fakhoury is a researcher and data scientist at Engineering Ingegneria Informatica S.p.A., where he designs and develops AI and data-driven solutions for industrial and enterprise applications. He holds a Ph.D. in Applied Mathematics from the University of Rome Tor Vergata, where he focused on neural network architectures and computational methods for AI. His expertise includes mathematics, machine learning, generative AI, and backend development. He has contributed to projects in academia and industry, including enterprise AI solutions at PwC, space weather forecasting at INGV, and knowledge graph–based AI systems at Banca d'Italia.