Apr 23, 2026

SAP: Bringing the ‘Operating System’ of a Company into the AI Era with CTO Philipp Herzig

SAP CTO Philipp Herzig discusses the company's AI-driven transformation, focusing on three core pillars: generative UI, AI-native business processes, and a unified data layer. He explores the primary challenges to enterprise AI adoption—scale, data fragmentation, and security—while emphasizing the critical role of verifiability and "agent mining" in creating reliable, compounding value. Herzig also details the limitations of LLMs for predictive analytics on tabular data and introduces SAP's alternative, Relational Pre-trained Transformers (RPT1).

Apr 22, 2026

Agents need more than a chat - Jacob Lauritzen, CTO Legora

Jacob Lauritzen, CTO of Legora, argues that as AI agents tackle more complex work, the bottleneck shifts from task execution to planning and review. He proposes a framework for human-agent collaboration based on increasing 'trust' and 'control', and advocates for moving beyond simple chat interfaces to high-bandwidth, domain-specific artifacts like documents and structured reviews for more effective collaboration.

Apr 20, 2026

What AI Agent Skills Are and How They Work

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.

Apr 19, 2026

Code Mode - Sunil Pai, Cloudflare

Sunil Pai from Cloudflare introduces "Code Mode," a paradigm where AI agents generate and execute code (like JavaScript) instead of using traditional JSON-based tool calling. This approach enables more efficient, stateful, and complex interactions with large-scale systems by leveraging the inherent capabilities of programming languages.

Apr 09, 2026

Building Agentic Applications with Spring AI • Matthew Meckes • GOTO 2025

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.

Mar 31, 2026

LLM Compression Explained: Build Faster, Efficient AI Models

Learn how AI model compression and quantization techniques are essential for optimizing Large Language Model (LLM) performance and significantly reducing inference costs in production. This deep dive covers practical examples, benefits like reduced latency and increased throughput, and strategies for different AI use cases, demonstrating how to deploy scalable AI with minimal accuracy degradation.