I spent three days debugging an AI agent that was working perfectly. The API calls were clean. The error handling was solid. The response times were excellent. Everything worked exactly as coded. Except the agent kept making the wrong decisions about 30% of the time. Turns out? The agent was executing flawlessly based on documentation that hadn't been updated since 2023. The code wasn't the problem. The source of truth was. If you're building AI agents, here's the uncomfortable reality: your biggest bugs aren't in your codebase—they're in your documentation. The Documentation Debt You Didn't Know You Had Let me show you what I mean. Here's a snippet from a process document I encountered recently: ## Refund Processing Workflow 1. Validate refund request against order history 2. Check if order is within 30-day return window 3. Verify product condition eligibility 4. Process refund to original payment method 5. Update inventory system Enter fullscreen mode Exit fullscreen mode Looks solid, right?…