Why 'AI unit tests' are non-negotiable for enterprise-grade AI

One of the greatest challenges in building with Large Language Models (LLMs) and agentic systems is their non-deterministic nature. The same input won't always produce the exact same output. For a consumer-facing chatbot, this might be a feature. For an enterprise-grade system making critical business decisions, it's a liability.

How can you ship a product you can't reliably test? How can you promise customers a predictable experience?

The answer isn't to abandon traditional testing principles, but to adapt them. For our autonomous legal-tech agent at Erstatnings-Assistance.dk, we developed what we call "AI unit tests" as part of a robust, containerized MLOps workflow.

What is an "AI unit test"?

A traditional unit test verifies a small, isolated piece of code. It might check if add(2, 2) returns 4. This is simple for deterministic systems.

An "AI unit test" is different. It doesn't just check the final output. Instead, it verifies the agent's behavior and reasoning process against an entire conversation.

Our AI unit tests work like this:

1. Define a conversation scenario: We create a test case that is a full, multi-turn conversation between a user and the agent. This includes the user's initial query, follow-up questions, and the agent's expected intermediate steps.
2. Assert on behavior, not just output: The test then runs the conversation and asserts that the agent:
   • Called the correct tools (e.g., the RAG document retriever).
   • Used the right parameters for those tools.
   • Followed the expected logical path through its state machine.
   • Produced a final response that is semantically correct, even if the exact wording differs.

The MLOps framework

These tests are integrated into a full CI/CD pipeline using Docker. This ensures that a change to one part of the agent's logic doesn't unexpectedly break another.

This framework directly addresses the critical business need for trustworthy and predictable AI. It allowed us to deliver a reliable system where behavior was verifiable. For any business looking to deploy AI in a high-stakes domain, this level of rigorous, behavior-driven testing is non-negotiable. It's the foundation of building customer trust and moving AI from a novel experiment to a reliable business tool.