The talk focuses on how OriginTrail Decentralized Knowledge Graph serves as a collective memory for AI and enables neuro-symbolic AI. We cover the basics of OriginTrail’s symbolic AI fundamentals (i.e. knowledge graphs) and go over details how decentralization improves data integrity, provenance, and user control. We’ll cover the DKG role in AI agentic frameworks and how it helps with verifying and accessing diverse data sources, while maintaining compatibility with existing standards. We’ll explore practical use cases from the enterprise sector as well as latest integrations into frameworks like ElizaOS. We conclude by outlining the future potential of decentralized AI, AI becoming the interface to “eat” SaaS and the general convergence of AI, Internet and Crypto.

Agents are transforming how we approach problem-solving, automation, and user interaction. In this talk, I will explore the practical applications of agents, focusing on how they can deliver value. We'll discuss when agents are the right tool for the job, scenarios where they are not the right tool for the job, and strategies for deploying them to production with confidence and reliability. Whether you're new to agents or looking to refine your approach, this session offers actionable insights grounded in real-world experience.

AI Agents as Neuro-Symbolic Systems: Expanding the Boundaries of Intelligence" The current discourse around AI agents often centers on LLM-based systems with tool-calling capabilities, like REACT agents. While effective, this narrow definition limits the potential of agents to solve complex, real-world problems. In this talk, we explore a broader, more robust perspective—AI agents as neuro-symbolic systems. By combining neural networks' adaptability with the precision of symbolic reasoning, neuro-symbolic architectures bridge traditional AI approaches and modern advancements, enabling scalable and versatile workflows. This expanded definition accommodates not only LLMs but also embedding models, decision trees, and hybrid systems that integrate various modalities of intelligence. We will delve into: 1. The evolution of AI agents and the limitations of current models. 2. The core principles of neuro-symbolic systems and their practical applications. 3. A reimagined framework for building intelligent agents that operate flexibly across diverse tasks. This session aims to redefine the way we think about AI agents, empowering developers and researchers to design systems that are more efficient, resilient, and capable of tackling dynamic challenges. Join us as we explore the future of agentic AI and its transformative potential.