Systems | Development | Analytics | API | Testing

UAT, or user acceptance testing, is the final phase of software testing where real users or business stakeholders verify that a product meets business requirements and works as expected before release. For example, imagine you’re testing a user registration page on a website to make sure new users can set up their account easily. A UAT scenario might confirm that users can: That’s user acceptance testing in action: validating that a real user can complete an important workflow successfully.

The rise of AI-assisted coding has transformed how software is built. With tools generating entire features in seconds, the bottleneck is no longer writing code—it’s verifying it. Because AI can generate boilerplate and handle API integrations instantly, more service changes are being pushed into authentication logic, API calls, and configurations. Teams desperately need a way to verify these changes before merging, especially when the code touches external dependencies.

New Sauce Labs Real Device Cloud Agent — available now in the watsonx Orchestrate Agent Catalog — can enable enterprise teams to trigger real-device tests, manage fleets, and validate app quality using natural-language commands.

Modern performance testing is evolving beyond the traditional choice between enterprise platforms and open-source tools. Teams increasingly need the flexibility of JMeter, k6, Gatling, or Locust combined with enterprise-grade reporting, scalability, security, and support. A new generation of platforms helps reduce operational complexity, lower total cost of ownership, and accelerate adoption through AI-assisted workflows and simplified onboarding.

This has been the reality of clinical decision-making for years: healthcare reacts after the signal becomes visible. Traditional clinical decision support systems helped standardize care and reduce errors, but most systems relied on static rules and issued alerts only after an event had occurred. They identify danger when it is already happening, not when it is quietly forming underneath the surface. That delay is expensive clinically, operationally, and financially.

AI agents do not just respond. They interpret goals, use tools, follow workflows, apply context, and sometimes change real systems. That means they need a different kind of testing.

Agentic Testing and QA is a practice in which AI agents operate directly on a project — reading files, planning tasks, generating framework code, and interacting with a browser — rather than simply answering prompts inside a chat window. Tools like Claude Code bring this capability to the terminal, giving QA teams a command-line assistant that understands repository context, proposes changes before applying them, and generates test assets across Playwright, Selenium, and API testing workflows.

Perforce Delphix vs. K2View — which one is better for your data management and compliance needs? Each provider has strengths and weaknesses, so it’s important that you find the right one that checks your boxes, prioritizes your top needs, and fits your use cases. In this blog, we’ll detail compare Delphix vs. K2view, including their key differences, use cases, integrations, and Delphix customer testimonies.