I watched an AI agent send 200 emails to the wrong recipients because I forgot one validation check. The emails were well written. The offers were real. The recipients were just... not our leads.
That was early. I learned fast. Every agent I build now has three layers of guardrails before it touches a database or an API. Here's exactly what those layers look like and why they're non-negotiable for production.
Input Validation: Your Prompt Is Not a Schema
The first mistake people make is trusting the LLM to produce valid output. It won't. Not reliably. I've seen GPT-4 return a JSON key called "emial" instead of "email" in a critical pipeline. One typo, and the whole record is garbage.
The fix is a strict validation layer that runs before any data reaches your system. In my AI resume tailor, I use a JSON schema with conditional presence flags. Every field that must be real has a has_* boolean guard. If the LLM tries to fabricate a phone number, the schema rejects it.
