Dex Horthy

This talk argues that current approaches to AI agent development, often focused on "harnessing" models or increasing scale, are insufficient for building maintainable software. The core thesis is that the failure of agent software factories stems from a fundamental problem in how coding models are trained, which prioritizes passing tests over architectural quality. This leads to codebases that degrade over time, becoming difficult to manage and debug.

This discussion explores the current state and future potential of "loops" in AI-driven software development, debating whether the hype surrounding them aligns with practical capabilities. The core thesis questions if we are at a significant inflection point towards fully autonomous software factories or if current loop implementations fall short of their promised potential. The debate highlights the tension between the rapid advancement of AI models and the necessary discipline and engineering required to build reliable, autonomous systems.

This talk addresses the challenges of using AI for complex software engineering tasks, particularly within large, existing codebases. The core thesis is that current AI models struggle with brownfield projects, often leading to rework and "slop." The presented solution focuses on advanced context engineering and intentional compaction to maximize the effectiveness of AI coding agents, enabling them to tackle difficult problems without introducing technical debt.

This talk proposes the "12-Factor Agents" pattern, drawing parallels to the 12-Factor App methodology for building reliable software. The core thesis is that building robust LLM applications requires applying established software engineering principles, focusing on modularity, control flow, and state management rather than solely relying on agentic capabilities. The approach emphasizes treating agents as software components that can be engineered for reliability and maintainability.