This talk explores how to overcome the context window limitations that prevent AI agents from accessing large APIs. It introduces "Codemode," a technique where agents write code against a typed SDK in a secure, sandboxed environment, moving beyond static tool definitions and enabling full API accessibility.
![[FULL WORKSHOP] AI Coding For Real Engineers - Matt Pocock, AI Hero (@mattpocockuk )](https://img.youtube.com/vi/-QFHIoCo-Ko/maxresdefault.jpg)
A workshop on building a complete AI-assisted development workflow, covering how to translate ambiguous requirements into agent-ready plans and run autonomous coding agents to ship production-ready features.
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).
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.
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.
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.