Tricentis platform uses AI to trust AI

“The goal isn’t to entirely remove the human element, but to have AI work side by side with in-house engineers,” says Damien Wong, Senior Vice President, APAC at Tricentis.

As organizations continue to increase their reliance on AI to develop code, one of the biggest challenges is reducing the risks that comes with it. Applications need to not only meet regulatory requirements but also have minimal errors as any downtime can lead to concerning consequences.

This is where vendors like Tricentis come in with their tools that can help businesses scale with AI seamlessly. The tech company recently launched its latest Tricentis AI Workspace platform. 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.

Specifically, 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.

The platform sees several AI agents working together with defined responsibilities across the entire software development lifecycle (SDLC). The agents include:

Opportunity for Asian businesses

To understand more about how businesses in Asia can benefit from Tricentis AI Workspace, CRN Asia speaks to Damien Wong, Senior Vice President, APAC (Asia-Pacific & Japan) at Tricentis.

How can APAC customers benefit from the new Tricentis AI Workspace?

With AI writing software faster than humans or traditional testing methods can keep up with, “almost right” software is being released at machine speed, creating huge business and reputational risks. Last year, enterprises across the globe faced a median cost of roughly $2 million for every hour of IT outage.

That said, established organizations in APAC are under great pressure to accelerate innovation, particularly as more agile AI-native startups are able to build and deploy applications much faster. They need to stay competitive, while maintaining control and reliability across their software environments.

Our new AI Workspace helps address this by orchestrating specialized AI agents across the entire software development lifecycle (SDLC). Companies can now implement stronger governance and have visibility into what AI is actually doing, as it is embedded with human review and approval gates. This enables our customers to scale AI adoption without increasing risks. Most importantly, it’s designed to be open and flexible. Not only can customers orchestrate within the Tricentis ecosystem, they can also orchestrate third-party agents, allowing them to integrate everything into a single coordinated workflow.

Will this tool now completely reduce the need to rely on manual code reviews and such?

No, the goal isn’t to entirely remove the human element, but to have AI work side by side with in-house engineers. The platform helps test engineers with tasks, such as in-context test authoring and natural-language test creation, so they can generate reusable test cases faster and with greater consistency, while reducing duplication and the reliance on very specialised expertise.

At the same time, AI agents are embedded across analysis, design, and execution, which can accelerate insight generation by as much as 90-95% and remove some of the manual expert bottlenecks that slow teams down. For example, a cloud migration testing cycle that might traditionally take months could potentially be compressed to about a week with agentic AI.

It’s important to note that humans still, and will always, play a critical role. The platform is designed to escalate situations to human test engineers when judgment or oversight is needed. That’s the unique selling point here.

So rather than replacing manual reviews entirely, it augments teams, allowing them to focus on value-added decisions, while AI handles much of the repetitive analysis and execution. That combination of automation and human expertise is really what will define the next wave of AI-driven software delivery.

How will this actually reduce risk in AI generated code?

The risk with AI-generated code is that it can be produced incredibly quickly, but speed doesn’t always equal quality. The reality is that in large enterprises, even a small flaw in one application can quickly spread to the entire ecosystem, leading to system downtime, operational disruption, or even reputational risk.

Many AI tools may look powerful, but they often lack the full application context and understanding of end-to-end dependencies, which is where risks begin to creep into processes.

What our platform does differently is provide a closed-loop software quality ecosystem. Tools like Tricentis SeaLights work within our AI workspace to identify which methods and paths were tested and which were not. The AI Workspace then feeds this data to AI agents to pinpoint test gaps. This process results in reduced test cycles, improved efficiency and lowered risk. Issues can be detected much earlier, and human teams are brought in when judgment or oversight is required. This end-to-end agentic quality oversight works across the SDLC.

So, the goal isn’t just to generate code faster, it’s to govern, test, and validate AI-generated code at enterprise scale, allowing organizations to move at the speed of AI without making compromises.

Will this tool be easy to deploy and used by organizations?

Our work shows that organizations face significant technical debt accrued over the years, and many are in the process of modernizing their legacy applications. That’s why it was important for us to build a platform that is quick and efficient for organizations to deploy.

While the Tricentis portfolio may be deployed and operated using traditional approaches, our new agentic capabilities simplify how organizations operate them. AI-powered agents now assist in areas such as test creation, validation, and analysis, allowing teams to automate manual, repetitive steps.

This enables organizations to utilize the platform more effectively, without requiring deep specialized expertise across every testing phase. By orchestrating and guiding users through complex tasks, the agents make it easier for teams to adopt advanced testing capabilities and integrate them into existing development pipelines.

This reduces complexity and accelerates time-to-value, empowering organizations to scale rapidly while software delivery reliability.

Lastly, how will partners help deliver this to customers in the region?

Partners are critical to delivering our enterprise agentic quality engineering platform and AI Workspace across Asia Pacific by bridging the gap between advanced technology and real-world implementation.

While Tricentis provides the innovation, systems integrators, managed service providers, and ISVs bring the domain expertise and services required to embed software testing and quality capabilities into complex transformation programs. They operationalize holistic quality engineering capabilities and increase productivity through agentic test automation, AI-driven testing, and autonomous validation within customers’ existing enterprise environments.

This is vital for large-scale modernization where applications are highly integrated across front-end and back-end systems. Partners ensure testing and validation are embedded continuously across the SDLC, enabling organizations to innovate with confidence while maintaining reliability.

Across the region, we work closely with partners, such as Brillar in Singapore and

global partner TTC, to expand testing capabilities, improve automation productivity, and accelerate adoption of AI-powered software testing.

We see partners as the force of multipliers, translating these innovations into tangible outcomes for enterprises across the region.