Software Testing Blog

Every test passes in staging. The release ships on Friday. By Monday, a payment webhook has silently dropped a chunk of orders, the OAuth refresh broke for sessions that stayed open over the weekend, and the partner API is returning HTTP 200 with "status":"failed" buried in the body. The QA report still says green.

If you are building a product powered by a large language model (LLM), you already know the thrill of shipping a new feature. You also know the creeping dread that follows. You push a small prompt tweak on Tuesday. By Friday, customer support is forwarding screenshots of your chatbot recommending a competitor’s product, hallucinating a refund policy that doesn’t exist, and forgetting to call the “cancel subscription” tool entirely.

Every week, QAwerk testers pick a game or app from the stores and hunt for bugs. We publish every finding on our Bug Crawl page, including steps to reproduce, video proof, severity, and other valuable details. We’ve already processed over 1,000 apps and logged 5,578 bugs after spending 15,000+ hours testing.

Every app crash is a silent goodbye, and on mobile, users rarely give you a second chance. Mobile app crash reporting closes that gap by replacing guesswork with the exact details of what broke, on which devices, and for how many people.

Let’s imagine that you ship an AI product that nails every demo. Your team runs it through its paces before launch, and the outputs look sharp, so you ship with confidence. However, two weeks later, a customer sends you a screenshot of a response that is factually wrong, confidently stated, and completely at odds with what the same product said the day before. That could be a serious blow to your reputation, and you absolutely cannot afford to lose customer trust.

If you are wondering why LLM red teaming tools are something you must know about today, consider this: cybercrime costs are forecast to exceed $10.5 trillion in 2025, with LLM vulnerabilities now part of that trajectory.

Air Canada lost a court case because its chatbot invented a refund policy. The tribunal ruled the airline had to honor what the bot promised. Klarna reversed its AI-first customer service strategy after its chatbot delivered worse service than humans, and started rehiring agents. Both stories made headlines because the underlying problem was the same. A large language model shipped into production without the QA process the technology actually needs.

One sentence. That’s all it took to convince a car dealership’s AI assistant to “agree” to sell a $76,000 SUV for a single dollar back in December 2023.

Multi-agent AI systems sell a tempting vision: autonomous agents collaborating like a seasoned human team. In theory, this setup allows a specialized researcher agent to gather data, a writer agent to draft a report, and an editor agent to finalize it, all seamlessly communicating in the background.

Is your API not performing as expected? Are issues piling up, and you have no idea why, because it passed every test your team threw at it?