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.
Jay Wengrow, author of “A Common-Sense Guide to AI Engineering,” breaks down how AI agents work, describing the 'clever hack' of intercepting LLM output to trigger functions. The discussion covers multi-agent architectures for complex tasks, implementing guardrails with regex and judge LLMs, and a pragmatic take on when to use frameworks versus building from scratch. Wengrow emphasizes understanding fundamentals over specific tools to create robust, production-ready AI applications.
Dexter Horthy of HumanLayer critiques the initial Research-Plan-Implement (RPI) framework for AI coding agents, revealing its tendency to encourage 'outsourcing thinking'. He introduces CRISPR, a new structured methodology that emphasizes smaller, focused prompts, human-agent alignment through artifacts like Design Discussions, and engineer ownership to combat 'slop' and improve code quality in complex projects.