Ask a naive agent to find a bug in a codebase with thousands of repositories, and it will find the first textual match and stop. It won't ask why. It won't check whether what it found is actually relevant. It won't explore related services. It'll just return something and call it done. We, at Wix, discovered this the hard way. And it's what pushed us to build something more deliberate: a research engine that doesn't just search, but reasons about what it's looking for and why

If you use AI to code, you’ve felt the "Context Wall." You start a project, things go great, but as the codebase grows, the AI starts making suggestions that conflict with previous decisions. You spend more time correcting the model than writing code To solve this, I developed the Design-Log Methodology . It started as a way for me to remember my own decisions in large projects, but it has become the single most important tool in my AI-coding workflow. The AI is no longer jus

Intro: The Limits of Prompt Engineering In modern AI agent development, many teams, including ours at Wix , start with an intense focus on prompt engineering , carefully crafting instructions for the LLM. We iterated endlessly on system messages and instruction sets to solve a core problem: our AI scheduling assistant was struggling with vague requests, such as "Schedule a meeting with Jake." This simple sentence triggers a surprisingly complex challenge. The system must iden