Systems | Development | Analytics | API | Testing

Picture this: You’re creating test cases for a new feature. You have a Jira ticket with text requirements, a Figma mockup from design, a workflow diagram from the architect, and a screenshot from a stakeholder meeting. Traditionally, you’d manually translate all of this into test steps: describing the UI in words, interpreting the diagram, cross-referencing the mockup. But what if your testing tool could “see and “understand” all these artifacts directly, just like you do?

It’s a common idea across the tech industry: AI is fueling a new wave of technological innovation. Test automation is no exception. Over three decades, enterprise QA customers have turned to Tricentis Tosca to transform inefficient testing practices with model-based automation. Now, the company you’ve relied on for scalable, codeless test automation is entering a new era of agentic testing with Tosca’s cloud deployment as its foundation.

In late 2024, Tricentis embarked on its own SAP transformation — migrating from NetSuite to a greenfield implementation of SAP S/4HANA Finance. This blog kicks off a series that will follow Tricentis’ migration journey, from planning and implementation through to outcomes and impact.

After attending Oracle AI World, one thing is clear: Oracle is all in on AI. From embedded agents across Fusion Applications to the AI-native Oracle Database and OCI’s massive model-training capabilities, Oracle is positioning itself as the platform where enterprise AI comes to life. The energy was infectious. Every session, demo, and conversation carried a sense of acceleration: faster decisions, faster workflows, faster innovation.

Just a few years ago, AI’s effect on software development was debatable — would it be as transformative as everyone predicted? But its impact is undeniable. In a 2024 survey, a majority of developers reported using AI in development, a sharp increase from the year before. In a short period of time, developers adapted, integrating agentic AI into their daily operations to boost productivity. Quality assurance teams are not far behind.

Tricentis SeaLights now supports SAP ABAP — expanding our SAP Change Impact Analysis solution by bringing SAP testing to the cloud. This new capability enables SAP teams to improve testing efficiency, reduce quality risks, and accelerate releases in modern cloud and hybrid environments. By delivering precise, actionable insights into what needs to be tested and where coverage gaps remain, SeaLights empowers teams to focus their efforts where it matters most.

In what has already been an innovation rich year, Tricentis NeoLoad continues to evolve to meet modern performance engineering needs. The 2025.3 release brings user‑centric insights with Core Web Vitals in RealBrowser which can leverage greater scalability through the recent availability of our high‑capacity cloud load generator. We are also continuing to build on our recently updated NeoLoad Web frontend with the addition of new and enhanced features.

We are pleased to introduce Tricentis Agentic Test Automation for Salesforce, an AI-driven solution that makes it possible to achieve high-quality Salesforce environments faster and with less effort. We recently announced Agentic Test Automation, enabling Tricentis Tosca to generate comprehensive, end-to-end tests using natural language. With this update, we are extending this capability and applying the power of agentic AI to your Salesforce environments.

In the race to deliver software faster, QA teams often face an impossible trade-off: run exhaustive test runs to mitigate risk or cut corners to meet deadlines. The hard truth: Neither option is sustainable. Running every possible test wastes time and resources and skipping tests without considering their relevance and business risk introduces defects that could harm your business. The root cause of the problem? Most testing tools miss the context that makes results relevant.

Analyzing performance test results has long been one of the most complex and time‑consuming aspects of performance engineering. As the volume of testing increases and delivery cycles continue to accelerate, teams face mounting challenges in making sense of vast amounts of data.