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.
As AI handles more of the coding, the role of a software engineer is shifting from writing code to planning and reviewing the work of AI agents. This talk explores the implications of this shift, the new workflows it demands, and the tools required to manage them effectively.
Rob Ennals, creator of Broomy, discusses a paradigm shift in working with AI coding agents: moving away from micromanagement towards orchestrating teams of parallel agents. The key is to design robust, automated validation systems and reshape the development environment to empower agents to work autonomously, efficiently, and at scale.
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.
Eric Zakariasson from Cursor explains the shift from single-agent pair programming to managing a multi-agent "software factory". He outlines the practical steps required, from establishing a well-structured codebase with guardrails to adopting a managerial mindset that focuses on automation, asynchronous work, and scaling agent fleets to increase software development throughput and consistency.
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.