Why giving AI coding agents architectural context in text is fundamentally broken

There's a problem with AI-assisted development that doesn't get enough attention: we're using text to describe something that is inherently visual and spatial. When you hand an AI coding agent a text description of your architecture, you're asking it to make dozens of implicit decisions you never specified. Which tables does this service query? How exactly does data flow from this form to that endpoint? What happens when this call fails? The AI fills every gap with a plausible-sounding assumption - and those assumptions compound across a complex project. The result looks correct at first glance. The code runs. But the architecture it implements isn't what you designed. It's what the AI guessed. This isn't an AI capability problem. It's an input problem. Text is a lossy format for architectural specification. We've always known this - that's why engineers whiteboard, draw diagrams, and use tools like Miro before writing a line of code. We think about architecture visually. But then we translate it into text just to hand it to an AI. The deeper issue is that current AI coding workflows have no formal specification layer between "idea" and "code." There's a gap where architectural decisions should be made explicitly but instead get made implicitly by the AI. I've been thinking about this problem for a while and built something to explore one approach - a visual canvas where you define your full architecture as typed nodes and explicit flows before any code is written, exporting a machine-readable spec the AI agent consumes directly. Happy to discuss the approach if anyone's curious: specrabbit.com But more broadly - how are others handling this? Has anyone found a reliable way to give AI coding agents unambiguous architectural context beyond writing longer prompts or providing existing code?