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Enterprise QA teams are discovering that deploying machine learning models breaks their existing validation pipelines. Legacy testing environments rely on a simple truth: fixed inputs must produce predictable outputs. Because intelligent architectures operate on probabilistic distributions, deterministic testing alone can no longer guarantee reliability. When conducting a code review or architectural risk assessment, treating an active model as a standard black-box API leaves critical flaws unaddressed.

AI made writing code effortless, but verifying it became the hardest job in software. So we rebuilt Sauce Labs around release assurance to close that gap.

May 2026 marks a turning point for how QA teams think about automation. This month's releases push Katalon True Platform closer to a world where you describe what you want to test, and the platform handles the rest: from generating test cases to running them, then handing you a ready-to-use script. Here is everything that shipped in May.

Creating test reports is an essential part of software testing, but manually compiling information from Jira can quickly become repetitive and time consuming. Whether you're preparing evidence for an audit, sharing release progress with customers, or documenting test coverage, reporting should help your team, not slow it down.

The digital healthcare market is undergoing a structural shift. Recent industry data from McKinsey and Statista shows that the global remote patient monitoring market is projected to reach $6.1 billion by 2030. Healthcare providers are rapidly moving away from legacy, episodic care models toward continuous, data-driven disease management. This change is accelerated by significant updates to reimbursement structures and a growing demand for scalable clinical workflows.

‍ ‍Satya Nadella said it plainly: "AI is not a feature. It is the platform shift of our generation." And he is right. Whether you are a $50 million mid-market firm or a $5 billion enterprise, the mandate from the board is the same: automate, optimise, and scale with AI. But here is the uncomfortable truth that most AI vendors will not tell you. Technology is seldom the bottleneck.

Picture a software testing failure at your company. The consequences range from money lost on weeks of rework to life-threatening catastrophe. These are the errors that keep engineers, their directors, and executives up at night. At some point, those risks came to life in the real world and pushed software teams to develop rigorous testing methodologies. But for some reason, we don’t think about data the same way. We think of it as reliable, durable, and highly unlikely to cause a big issue.

Steering lets a client redirect an agent while the agent is still working, so a follow-up message reshapes the answer in flight instead of cancelling the Run and starting over, or waiting for it to finish. In the AI Transport SDK, a Run encapsulates the agent's output for a single turn (including multiple iterations around an agentic loop). The last release, v0.5.0, made a single agent turn survive a crash by splitting a Run into re-attemptable Steps.

If you're running a customer-facing AI agent on Temporal, you've probably seen this: a user refreshes the tab mid-response. When the page comes back, the response resumes, but anything they were about to send back (an interruption, a tool approval) is gone. Temporal keeps the workflow itself crash-proof, but getting its output to a browser, and taking anything back from the browser into the workflow, has always meant building it yourself.