Tricentis Introduces End-to-End Enterprise Agentic Quality Engineering Platform

Tricentis, a global leader in agentic quality engineering, is introducing its unified, agentic quality engineering platform powered by the new Tricentis AI Workspace.

By orchestrating a team of intelligent AI agents, the new platform empowers enterprise teams to deliver rapid innovation while managing risk and resources, fundamentally redefining how high-quality code can be tested, governed, and released, at the speed of AI, the company said. 

The Tricentis Agentic Quality Engineering Platform combines powerful AI agents with decades of Tricentis expertise and proprietary technology across nearly 200 ERPs and packaged applications, while also extending to web and custom apps to accelerate and scale software development and quality autonomously while human employees retain oversight, judgment, and accountability. 

According to the company, Tricentis AI Workspace operates as a single, unified command center with shared context, integrated workflows, and native agent-to-agent collaboration to serve as the system of record and ‘control tower’ for agentic quality engineering, coordinating AI agents across testing, automation, performance, and quality intelligence, while embedding governance, approvals, and auditability directly into execution. 

“AI is transformative in its ability to create code at unprecedented speed, however the friction caused by lack of confidence in the quality of the output is causing CIOs real pain. While enterprises demand speed, they also can’t afford to introduce risk through unsecure or low-quality AI-generated code,” said Kevin Thompson, chief executive officer at Tricentis. “That’s the problem Tricentis is solving today. We’re offering the first end-to-end agentic software quality platform that redefines how enterprise software can be tested, governed, and released to deliver high-quality code at the speed of AI while safely accelerating time-to-value.” 

Within Tricentis AI Workspace are several AI agents working together with defined responsibilities across the entire software development lifecycle (SDLC):   

• Tricentis Agentic Quality Intelligence: Continuously interprets change, risk, and quality signals across the SDLC to determine release readiness, automatically directing testing and escalating to humans only when judgment is required. 
• Tricentis Agentic Test Automation (updated): Building on the initial launch of Agentic Test Automation, this next generation increases productivity. New features include support for SAP GUI and web applications, deeper integration with Tricentis Tosca automation engines, and intelligent reuse of test modules to reduce duplication, maintenance, and risk. 
• Tricentis Agentic Performance Testing: Delivers enterprise-ready, AI-driven performance validation by embedding autonomous agents across analysis, design, and execution—accelerating insights, eliminating manual expert bottlenecks, and enabling faster, more confident AI-era release decisions from API to end-to-end systems. 
• Tricentis Agentic Test Creation: Integrated deeply into Tricentis qTest, Agentic Test Creation lives side by side with test engineers, helping them with in-context test authoring. Enables natural-language test creation, allowing teams to generate reusable test cases faster and more consistently while reducing duplication and reliance on specialized expertise.  

“We’re already using agentic testing at Tricentis and are experiencing real impact in our transformation projects,” said David Cowell, VP of AI and machine learning at Tricentis. “A cloud migration that would typically take a few months took us just one week with agentic AI. That’s the kind of step-change enterprises need, compressing release cycles without increasing risk, and enabling teams to move faster without cutting corners on quality.” 

These agents and the capabilities they unlock represent the foundation of a broader agentic quality platform, designed to evolve as enterprises move toward fully autonomous, continuously governed quality engineering over the next several years, the company said.

Benefits include: 

• Faster quality throughput at enterprise scale
• Operational control and governance over AI
• Lower-risk AI adoption
• Accessible AI across quality teams  

For more information about this news, visit   www.tricentis.com.