If You're Building with LLMs, You Should Have Thought About Observability from Day One
"AI did something. But I have no idea what it actually did." — Before that happens to you, observability is something you really need to think about upfront. When you develop using AI coding tools like Claude Code or Cursor, you can see "it works." But "why it worked," "where and how it made decisions," and "whether the implementation matches my intent" become much harder to see. The same goes when you use AI in your product itself — whether that's calling LLM APIs, building AI agents, or integrating MCP tools. If you can't see what the AI returned or what decisions it made, you can't improve it. There's a principle in the SRE world: "If you can't see it, you can't fix it." This principle applies directly to AI-powered development and to products that run AI-driven features. This article captures how I've come to think about observability in LLM-based development — and what I'm actually…
