Yaser Al, founder of Chatbase, shares his journey of bootstrapping the company from a side project to a $10M ARR business. He details his product-led growth strategy, the evolution of the tech stack from early RAG to a multi-model harness, and his vision for AI agents as 'Chief Customer Officers'.
Richmond Alake from Oracle explains the critical role of Agent Memory in building adaptive AI agents, detailing the four types of memory, the limitations of Retrieval-Augmented Generation (RAG), and the architecture of the modern agent stack.
This hands-on workshop details the construction of a sophisticated, dual-part AI system for producing high-quality technical content. It begins with an MCP-powered deep research agent that autonomously plans, searches the web, and analyzes sources like YouTube to synthesize a grounded research artifact. The second part is a constrained, deterministic writing workflow that transforms this research into polished, non-sloppy content using an innovative "Evaluator-Optimizer" pattern for iterative refinement. The session emphasizes crucial AI engineering principles, such as choosing between agentic and workflow-based architectures, and concludes with a deep dive into implementing practical observability and evaluation pipelines to ensure the system is both measurable and improvable.
AI agents, powered by LLMs, excel at reasoning but lack the procedural knowledge required for real-world workflows. Martin Keen explains how the 'agent skills' open standard solves this by packaging step-by-step instructions, enabling agents to automate complex tasks efficiently and reliably.
Matthew Meckes from AWS makes a compelling case for Java's central role in the future of enterprise AI. This talk explores how Spring AI empowers developers to build robust, production-ready agentic applications by integrating LLMs with existing Java services, moving beyond proofs-of-concept to solve real-world business problems.
Cedric Clyburn explains the Models-as-a-Service (MaaS) pattern, detailing how organizations can build their own private AI infrastructure to deploy models like LLMs securely and at scale. He covers the benefits over public APIs, including cost control, data sovereignty, and lifecycle management, and outlines a technical architecture using Kubernetes, API gateways, and observability tools.