Developer builds AgentFlow micro-framework to improve AI coding agent accuracy

A developer who writes as little as 2% of code by hand has spent ten months refining a process to reduce errors in AI agent-generated code across complex and commercial projects. The core problem identified is that agents lack deep project context, much like a new hire on their first day, and tend to latch onto the nearest familiar solution rather than the most appropriate one. Task misinterpretation compounds the issue, as agents rarely ask clarifying questions before diving into implementation. To address this, the developer packaged their workflow into AgentFlow, a micro-framework designed to give both developers and agents a shared, structured way to work through tasks. The framework draws on approaches gathered from multiple sources, with the author's contribution being their consolidation into a single tested, practical process.