Systems | Development | Analytics | API | Testing

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.

White box testing is what separates teams that know their code works from teams that hope it does. High code coverage numbers can be misleading. A suite with 90% statement coverage can still miss the branch that throws a NullPointerException in production, or the loop condition that behaves differently on an empty list. White box testing is not just about running code – it’s about systematically verifying that every path, condition, and branch in your logic behaves the way you intended.

Automated load testing has reached a turning point in 2026. Artificial intelligence, once a gradual addition, now drives a clear shift in how organizations validate performance. Industry reports project a 15% compound annual growth rate (CAGR) for AI in software testing from 2023 to 2026, underscoring the urgency to modernize testing practices and keep up with rapid development cycles.

State and local governments are under growing pressure to modernize the systems that power essential services, from healthcare and human services to transportation and public safety. At the same time, citizens expect fast, seamless digital experiences when interacting with government agencies. To meet these expectations, many agencies are investing in large-scale ERP transformations, including SAP and Oracle migrations.

We surveyed 2,501 IT decision-makers, QA professionals, and business leaders across six countries for our second annual Quality Transformation Report. Respondents came from organizations with 150-plus employees across manufacturing, energy and utilities, retail, financial services, and the public sector. One of the major findings: confidence in AI agents making release decisions dropped from 48% in 2025 to 34% in 2026. That’s a 14-point decline in a single year.