Tricentis unveils AI Workspace for agentic quality testing
Tricentis has launched an agentic quality engineering platform centred on a new AI Workspace. It is positioned as a single environment for coordinating AI-driven software testing and related quality processes, with governance and human oversight.
The launch comes as many large organisations expand the use of generative AI in software development. The shift is raising new concerns about quality, security and compliance as teams increase release frequency and the volume of code changes.
Large enterprises often run hundreds of interconnected applications, including packaged systems such as ERP and CRM tools alongside custom software and web applications. A defect in one system can trigger knock-on issues across downstream integrations, causing operational disruption and increasing risk. Quality teams are also under pressure to keep pace with changing requirements and increasingly automated delivery pipelines.
Tricentis says its approach uses a coordinated set of AI agents working from a shared context in the AI Workspace. The Workspace acts as a central command centre for quality engineering, including testing, automation, performance and quality intelligence. It also includes governance features such as approvals and auditability within execution workflows.
AI Workspace
The AI Workspace is intended to serve as a system of record for agent activity across the software development lifecycle. It is designed to support collaboration between agents and provide visibility into their actions, with escalation to humans when judgement is required. Tricentis says this structure reduces reliance on generic AI tools that may not understand application-specific context or end-to-end dependencies across enterprise systems.
The platform draws on Tricentis' existing technology and domain coverage across close to 200 ERP and packaged applications, and also supports web and custom applications. Tricentis presents this breadth as important for enterprises that need consistency across heterogeneous application estates.
CEO Kevin Thompson said quality concerns are becoming a bottleneck as AI increases the speed of code generation.
"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."
Meet the Agents
The platform groups several agents under the AI Workspace, each assigned to a different part of quality engineering. Tricentis Agentic Quality Intelligence focuses on interpreting change, risk and quality signals across the lifecycle. It is intended to assess release readiness and direct testing activity, escalating to humans when judgement is required.
Tricentis Agentic Test Automation is an updated version of an earlier product. Tricentis says it adds support for SAP GUI and web applications and integrates more deeply with Tricentis Tosca automation engines. It also includes mechanisms for reusing test modules, which the company says reduces duplication and maintenance.
Tricentis Agentic Performance Testing targets performance validation. Tricentis says it deploys agents across analysis, design and execution, delivering faster insight generation and reducing dependence on specialist manual work. Coverage ranges from API performance to end-to-end system scenarios.
Tricentis Agentic Test Creation sits within Tricentis qTest. It is designed to provide in-context authoring assistance for test engineers. Tricentis says it supports natural-language test creation and produces reusable test cases more consistently.
David Cowell, VP of AI and Machine Learning at Tricentis, cited internal use of agentic testing as evidence of its impact on delivery timelines.
"We're already using agentic testing at Tricentis and are experiencing real impact in our transformation projects," Cowell said. "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."
Governance Focus
Tricentis is emphasising governance and auditability, positioning the platform for controlled AI adoption in regulated and high-risk environments. It says the Workspace centralises policy enforcement and approvals, provides visibility into when human intervention is required, and includes human review gates in execution workflows.
Tricentis also argues that usability will affect adoption. Teams can create and manage agents without writing code, it says, reducing dependence on specialist skills and custom development work.
Paul DiGrazia, Vice President of Quality Engineering at Wolters Kluwer, described the shift as one of trust and readiness in a faster software creation environment.
"The Tricentis AI Workspace is a strategic inflection point for us because it enables agentic orchestration across the entire software development lifecycle - not just code generation or test automation. As AI accelerates software creation, the real challenge becomes trust - software is often nearly right, but not production-ready. Our approach shifts Quality from manual test creation to confidence engineering. Instead of simply generating tests, our agents actively identify unknowns, prevent defect classes, orchestrate risk-based validation, and provide readiness signals that support human decision-making. This allows us to move at the speed of AI while shipping responsibly - reducing risk, improving release confidence, and delivering measurable ROI through faster delivery, fewer escapes, and continuous learning at scale."
Tricentis says early deployments have achieved up to 60% automation of regression test grids. The company says the platform is designed to evolve over the coming years toward more autonomous quality engineering with continuous governance.