Your AI Agent Will Lie to You in Production — Here's How to Catch It Before It Ships

You deploy an AI agent. It passes your manual tests. It looks good in the demo. Three weeks later, someone edits the system prompt to make the output "cleaner." The agent starts behaving differently on edge cases. No error. No alert. Just subtly wrong output — until someone notices. This post is about the CI/CD and prompt regression setup that prevents this. Everything here is practical and works today on AWS. The Problem With AI Agents in CI/CD Traditional software has a clear contract: given input X, function F returns output Y. Tests verify Y. If Y changes, the test fails, the build breaks, you investigate. LLM-based agents break this model. The "function" is a language model. The same input can produce slightly different outputs on every run. And the failure mode isn't an exception — it's a plausible-looking wrong answer. Three things make this worse in serverless AI pipelines: 1. Prompts aren't versioned like code. Engineers edit them in a string in a Python file, or worse, in a c