What are the most overlooked failure patterns when testing AI applications? 🤖

While testing an agentic AI platform, I noticed something interesting: not every AI application failure is actually a model problem.

Sometimes, the model gives the right answer, but the application still fails because of issues with tool calls, integrations, authorization, stale UI state, or usage tracking.

Here are a few examples:
🔹 The UI reports success even though the backend operation failed.
🔹 An agent selects the wrong model or gets stuck during a long-running task.
🔹 A tool returns unexpected data, but the application doesn't handle it correctly.
🔹 A query returns misleading results because of a data or semantic-layer issue.

These are easy to overlook when testing focuses primarily on the quality of AI-generated responses.

I recently wrote an article covering 9 AI application failure patterns and how to test for them, based on my experience testing AI products.

I'd love to hear from fellow QA engineers, SDETs, and AI engineers:

What's the most unexpected bug you've encountered while testing an AI-powered application?

Let's share some real-world lessons! 👇

Read the full article: https://medium.com/@gundetisooraj/9-ai-application-failures-that-arent-model-problems-and-how-to-test-them-120508b7536a?sharedUserId=gundetisooraj