Systems | Development | Analytics | API | Testing

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.

You’ve likely heard a lot about MCP (Model Context Protocol) lately. It’s one of the many AI functions that are being added into the testing landscape every day. The open-source standard for connecting AI applications to external systems has been getting significant buzz in enterprise software testing, with Tricentis leading the way with its industry-first secure remote MCP servers.

Autonomous agents — AI-driven bots, virtual assistants, and task-specific automators — are quickly becoming part of everyday business operations. They promise speed, efficiency, and the ability to scale tasks across teams without human intervention. But there’s a new challenge emerging in this AI-driven workplace: AI agent sprawl.

The future of testing is AI-driven, business-oriented, and no-code. Exclusively available for Tosca in the cloud, Tosca’s Model Context Protocol (MCP) Server is a game-changer. It provides a consistent, standardized, and open framework that connects your AI models to Tosca — no need for custom and inflexible API integration. Whether generative, agentic, or any future AI innovation, MCP bridges the gap between AI and QA tools, enabling seamless integration and smarter, faster testing.

At a recent gathering of public sector data leaders, the conversations didn’t revolve around which artificial intelligence tool is fastest or how to adopt zero trust frameworks. They focused on something far more foundational: trust. Specifically: Can we trust our data? At the CDAO Government event, federal and state agency leaders focused on the truth behind effective decision-making and efficient public service delivery: governance only works if the data is complete, accurate, and ready for use.

As modern software delivery processes accelerate, QA teams inevitably feel pressure to maintain quality at scale. SeaLights addresses this challenge by providing deep visibility into test coverage across every stage of the software development lifecycle. It helps teams identify untested code changes, optimize execution, and deliver confidence at release time.