A practitioner's guide to building a context engine, the reasoning layer that provides AI agents with the necessary organizational context to generate effective and appropriate code. The talk debunks common myths about RAG and large context windows, outlines core requirements for a robust context engine, and shares lessons learned from production.
Patrick Debois argues that as AI coding agents become more capable, the context that drives them—prompts, rules, and memory—needs its own engineering discipline, akin to how we manage code. He introduces the Context Development Lifecycle (Generate, Evaluate, Distribute, and Observe) to make context a shared, repeatable, and improvable part of software delivery, creating a flywheel effect where better context leads to better agent output and continuous improvement.
Liam McGarrigle demonstrates how to build secure, observable, and controllable AI agents in n8n. The workshop covers creating a human-in-the-loop workflow for managing Gmail and Google Calendar, focusing on n8n's visual system for tool configuration, prompting strategies, and implementing essential approval steps to prevent unintended actions.
A deep dive into the practical, long-term experience of giving a personal AI agent (Open-Claw) incremental control over one's digital life. The talk covers the gradual "permission creep," the pivotal role of an Obsidian knowledge base for providing context, the ecosystem of overnight cron jobs for self-maintenance, and the philosophy of using an agent to "optimize for your future self."
Danilo Campos of PostHog details the common failure modes of LLM-based code generation—from model rot to security risks—and shares the practical, prose-driven strategies his team uses to make their autonomous coding agent reliable for thousands of users.
David Gomes from Cursor explains their transition from a complex, 15,000-line Git WorkTrees feature to a lightweight, flexible solution built on Markdown prompts. He details how 'Skills' and 'Sub-agents' recreated parallel coding workflows, and discusses the trade-offs, failure modes, and lessons learned from shifting product logic from hard code to natural language instructions.