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.
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.
![[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.
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.
Learn practical strategies to adapt third-party MCP server tools for production AI applications. This talk covers five key practices: curating tools, enhancing descriptions, implementing deterministic guardrails, composing new tools from existing ones, and leveraging tools as simple functions, all demonstrated through a real-world "Spec Reviewer" example.