Your Agent Doesn’t Need a Bigger Context Window — It Needs a State File

AI coding agents are powerful but brittle — they lose context, can’t recover from failures, and fall apart on complex multi-step projects that span multiple files, services, or systems. Most teams reach for longer context windows or multi-agent frameworks. There’s a simpler answer. This talk presents a production-tested pattern using three ideas: skills-based phase orchestration to break complex work into manageable steps, file-persisted state for resumable execution, and build-log feedback loops for self-correction. Walk away with a concrete, framework-free approach to turning unreliable agents into resilient pipelines that can stop, resume, and fix their own mistakes — even on projects too large for a single context window.