TLDR: Tricentis has announced a new roadmap for AI-first enterprises, focusing on agentic workflows to revolutionize software quality engineering. The company introduced a unified AI workspace and agentic ecosystem, featuring industry-first remote Model Context Protocol (MCP) servers and Tricentis Agentic Test Automation. These innovations aim to enable autonomous, AI-driven testing across complex enterprise systems like ERP platforms, addressing significant challenges in software quality and development speed.
Tricentis, a global leader in AI-enabled software quality engineering, has unveiled its ambitious roadmap for AI-first enterprises, shifting from traditional ERP challenges to advanced agentic workflows. This strategic move, highlighted at Tricentis Transform, the company’s flagship global event in London, marks a pivotal moment in how organizations will approach software development, testing, and delivery in the AI era.
At the core of this vision is a newly introduced unified AI workspace and agentic ecosystem. This comprehensive platform integrates Tricentis’ portfolio of AI agents, Model Context Protocol (MCP) servers, and AI platform services, establishing a centralized hub for managing quality at the speed and scale demanded by modern innovation. The company emphasizes that this is a significant leap towards autonomous testing, moving beyond mere assistive AI to systems that actively generate, execute, and continuously learn from test outcomes.
Key innovations include the launch of remote Model Context Protocol (MCP) servers for enterprise testing and Tricentis Agentic Test Automation. Tricentis claims to be the first major quality engineering platform to offer secure remote MCP servers, which serve as the ‘UI for AI’ infrastructure. This allows AI agents to directly interact with enterprise-grade testing tools such as Tricentis Tosca, NeoLoad, and qTest. An on-premises option is also available for SeaLights. This open, modular framework provides customers and partners with the flexibility to co-develop solutions or build their own AI agents, compatible with various AI models like Anthropic’s Claude AI assistant or third-party agents powered by OpenAI.
The second pillar, Tricentis Agentic Test Automation, introduces the industry’s first AI agent capable of generating complete test cases from natural language prompts. This agent can analyze prior test runs and adapt to enterprise-specific contexts, ensuring end-to-end test coverage across both modern SaaS tools and legacy platforms. These capabilities are particularly crucial for testing mission-critical ERP systems such as SAP and Oracle, where the shift from monolithic suites to composable systems necessitates more adaptive and autonomous testing.
According to the 2025 Tricentis Quality Transformation Report, the need for such advancements is pressing. The report reveals that nearly two-thirds (63%) of organizations deploy code without fully testing it. Furthermore, over 8 out of 10 (81%) report financial impacts from software defects exceeding $500,000 annually. As AI accelerates development and delivery cycles, the demand for adaptive, autonomous testing becomes paramount to close this ‘quality gap.’
Kevin Thompson, CEO of Tricentis, stated, “With MCP and Agentic Test Automation, we’re not offering a one-size-all approach, we’re giving our customers the flexibility to use or build their own AI agents. This is about AI acting, not just assisting. AI-led, autonomous testing is no longer on the horizon, it’s here.” Dave Colwell, VP of AI & ML at Tricentis, highlighted the separation of concerns in agentic workflows, noting, “That separation creates the same dynamic you get in human teams, where developers and testers think differently.”
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The intelligent workspace, slated for release in 2026, will enable organizations to onboard and orchestrate AI agents from Tricentis, partners, or third parties; define governance and security policies for responsible AI operations; integrate directly into SDLC workflows using tools like Jira, GitHub, and ServiceNow; and monitor agent performance and compliance through unified dashboards. These innovations are designed to democratize access to AI, driving software quality through an end-to-end, hybrid-ready approach, and mark the beginning of a broader roadmap to embed agentic AI throughout the entire software quality lifecycle.
