Wolters Kluwer
Wolters Kluwer drives consistent software quality at scale with agentic AI
applications under test
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faster test case design with Agentic Test Creation
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automation for UI and API test cases on core platform applications
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Company overview
Wolters Kluwer is a global leader in professional information, software solutions, and services for healthcare, tax and accounting, financial and corporate compliance, legal and regulatory, and corporate performance and ESG sectors. The company helps customers make critical decisions by providing expert solutions that combine deep domain knowledge with specialized technology and services. Headquartered in Alphen aan den Rijn, the Netherlands, the company serves customers in over 180 countries and maintains operations in over 40 countries.
Industry: Information services- Organization size: 20,000+ employees
- Location: Netherlands
- Products:
Building a quality engineering strategy for the age of AI
Wolters Kluwer started almost two centuries ago and was originally a print company. Over the past few decades, the organization has been on a sweeping digital transformation journey and introduced new technologies that rolled out at a rapid pace. The company has grown exponentially over the last few years, and a large part of that success can be attributed to ensuring that everything it delivers to customers is of high quality.
With the influx of AI in the org’s toolset, particularly applied to software development, Paul DiGrazia, Vice President of Quality Engineering at Wolters Kluwer, set out to build a quality engineering strategy to match the increasing volume and velocity of AI-generated code – and to catch the errors that AI can sometimes introduce – and that are difficult to catch with traditional testing methods. To that end, he turned to qTest’s agentic AI capabilities to help his team create test cases faster and ensure broad coverage across diverse use cases.
DiGrazia and his team of veteran qTest users were early adopters of the AI, quickly deploying to 400 users within the Wolters Kluwer quality engineering org. In a short period of time, the org achieved 30% time savings in test case design by directing agentic AI to draft test cases, steps, and expected results from user requirements. As Wolters Kluwer moves toward a fully integrated development environment (IDE), where debugging takes place right alongside code creation, the ability to accelerate quality with AI is pivotal to success.
Tapping into the potential of agentic AI
Recently, Wolters Kluwer’s quality engineering teams participated in the beta program for Tricentis Agentic Test Creation (ATC), providing valuable feedback that has helped shape the product’s direction. DiGrazia says “ATC has been a complete gamechanger because we’re not spending any time doing manual test case design. ATC is so specialized and knows the right way to write test cases based on their corresponding requirements and business cases. It’s been a huge time saver for us.”
With Agentic Test Creation now generally available, DiGrazia is rolling ATC out across the company’s qTest user group – which has grown to 500+ in the last year – to accelerate test design across development projects.
Alongside ATC, Wolters Kluwer is deploying AI Workspace, a centralized agent management platform, to orchestrate work across all of the agents his QE org interacts with. He sees it as a platform with “limitless potential,” given its ability to unify agentic workflows and governance across virtually any agent across the software delivery pipeline – and his team is already putting that potential to work.
Today, his team is using AI Workspace to embed autonomous quality at nearly every step of their software development lifecycle. During development, AI Workspace ingests requirements from GitHub and kicks off workflows in Agentic Test Creation (ATC) to generate test cases upfront — capturing intended behavior before a single line of code is written.
From there, ATC and AI Workspace work in concert throughout the SDLC, continuously comparing how test cases evolve as requirements move from development through to final regression. This gives DiGrazia’s team a way to quantify risk and detect what they’ve dubbed “intent drift” — the gradual divergence between what an AI agent produces and the original business and product requirements it was built to serve. By surfacing this drift early, the team can build targeted test cases that close the gap before it reaches production.
“We can now build test cases based on outcomes rather than tools, and orchestrate agents in testing as well as development, to craft a predictive and proactive quality strategy, all while ensuring humans still steer our agents,” DiGrazia says.
The result is faster, sharper visibility into test coverage earlier in the SDLC. By the time DiGrazia’s team reaches full regression, risk has already been scoped and intent clarified for each test case. And that feedback has been delivered to developers in time to actually shape the test suite, rather than react to it. That’s possible, DiGrazia explains, because AI Workspace’s role extends well beyond test orchestration.
“AI Workspace just doesn’t orchestrate the execution of autonomous testing – it’s an entire agentic SDLC enabler,” DiGrazia says. “It can orchestrate developer agents as well, which enables true end-to-end agentic workflows across all of our MCPs and APIs, allowing us to enter production deployment with higher levels of visibility and confidence”
Orchestrating quality across an extensive application landscape
DiGrazia and his team oversee quality standards across hundreds of applications and platforms spanning a wide range of technologies — from Azure and AWS to Java, .NET, SQL, and Oracle — including both modern cloud infrastructure and systems that have been in production for two decades.
Quality metrics are central to how the team operates. “The most effective metric is our escaped defect,” DiGrazia explains. “We also look at customer experience metrics and have a preventative action process aligned with it.”
qTest has been the anchor for DiGrazia’s test management strategy across this sprawling environment, and AI Workspace is now being woven into that foundation as AI usage increases at every stage of the SDLC. DiGrazia sees it making an early “big impact as we move to evolving quality into an entirely agentic process at Wolters Kluwer.”
One early example: within AI Workspace, DiGrazia built a coordinated escaped defects tracking workflow across multiple AI agents. “Within an hour, we had a whole agentic escaped defects tracking process outlined,” he says — automatically documented in qTest, and immediately useful for the go/no-go release decisions his team depends on.
Transforming testing at scale with Tricentis qTest
Wolters Kluwer’s testing environment was once a patchwork of manual spreadsheets, Word documents, and disparate tools. The organization needed a unified test management solution to centralize testing sources, standardize best practices, and scale automation across the enterprise. It selected qTest.
Built around a test automation pyramid philosophy — with heavy investment at the API and unit testing levels, and targeted UI testing where it delivers the clearest ROI — the team has achieved 100% automation at the UI and API levels for its core platform applications. Automation rates across other applications range from 30% to 60%, with a focus on repeatability.
“We focus automation on highly matrixed and repeatable regression tests,” DiGrazia explains. “That frees humans up for finding negative test cases, and lets us get through test cycles more quickly without a massive regression burden.”
qTest serves as the team’s central source of truth for testing metrics — execution rates, test development progress, and automation coverage — with data fed via the qTest APIs into an internally built tool married with Jira. “We have a full picture of tests run and what the quality is on the other side,” DiGrazia says.
Maintaining consistency and quality at scale
DiGrazia leads Wolters Kluwer’s Quality Engineering Center of Excellence within the Digital Experience Group, the technology arm of the organization. A core part of his mandate is centralizing standards and tooling across the enterprise’s diverse business units — a challenge he’s tackled with both training and a guiding mantra: “excellence through innovation.”
“We wanted everything we do and every decision we make to back into that mantra,” DiGrazia says. “It drives a consistent way of thinking that trickles down through the whole organization.”
Scaling that consistency across several hundred people is where qTest has proven indispensable. DiGrazia hosts regular agentic AI training sessions and has developed guidelines for training AI models and using AI tooling — including optimized prompting techniques — in close collaboration with the Tricentis team. “There is no way we could scale if we didn’t have tools like qTest,” he says.
The results are measurable. Escaped defect and fail containment numbers have improved, and releases have become more consistent and predictable. “Before qTest, it was a lot of human judgment — gut feeling,” DiGrazia reflects. “Now it’s all data-driven, and that’s driven down our escaped defect rates.”
Embracing a future of constant change
DiGrazia and his team continue to partner closely with Tricentis as early adopters of emerging agentic capabilities, including the Tricentis Agentic Quality Engineering Platform. “By partnering with them early, our testing team gains early access to new capabilities. Our engineers can explore ideas and iterate faster with Tricentis tools.”
On the broader moment the industry finds itself in, DiGrazia doesn’t mince words: “I’ve been doing this a long time, and this is the most disruptive time in software quality I’ve ever seen, by orders of magnitude.”
His advice to quality leaders navigating the shift? “Run towards change. There is a promising future ahead for the early adopters applying AI in quality engineering.”
