You Cannot Test AI the Way You Test Software

The Assertion Problem

Traditional software testing is built on assertions. Call a function, assert the output matches known values. Deterministic. Reproducible. Automatable.

Now try to write a test for an LLM response.

Ask a language model a question and you get a different answer every time. A traditional assertion cannot validate that the answer is correct, complete, and consistent with company policy, without being so rigid it fails every time the model is updated.

This is the assertion problem. And it is just the beginning of why AI systems require an entirely new approach to quality.

The Four New Problems

Non-determinism makes regression testing break. Agent path complexity means you need to validate the decision path, not just the final output. Model version drift means quality can degrade silently with no code changes on your side. And adversarial robustness requires systematic red-teaming for prompt injection and jailbreak attempts.

TestForge AI, QA Studio, and Sentinel

At Ivalio, we have built three distinct tools for AI-era quality. TestForge AI is our code-first framework that generates, executes, and self-heals test cases using AI. QA Studio lets non-technical teams design and manage tests visually, with AI generating edge cases automatically. Sentinel deploys autonomous agents that continuously probe your AI systems for quality degradation, prompt injection vulnerabilities, and model drift.

Together they give enterprises what traditional QA cannot: coverage of the non-deterministic, continuous monitoring of AI behaviour, and acceleration of testing on traditional software alongside.