Everything we announced at our Agentic Quality Engineering Platform launch

Over 1,000 people around the world tuned in as Tricentis CEO Kevin Thompson and VP of AI David Colwell unveiled our new integrated platform, followed by a live demo from Enterprise Solution Architect Matt Serpone.

From our headquarters in Austin, Texas, we unveiled a unified solution designed to help enterprises treat quality as a coordinated system rather than a collection of disconnected tools. At its core is a managed workforce of AI agents that execute, adapt, and collaborate across quality domains, governed by a control plane (AI Workspace) that orchestrates those agents, enforces governance, and keeps humans in the loop.

Tricentis customers were in attendance, including Wolters Kluwer, an early adopter of AI solutions. Wolters Kluwer is configuring ways to use Agentic Test Creation and AI Workspace to achieve total agentic automation across the entire SDLC, shifting left as far as possible.

Here’s an overview of what was covered at the launch.

What this platform was designed to fix

Kevin Thompson opened by laying out the pressures testing teams face right now: speed is accelerating, but quality infrastructure hasn’t kept pace. AI-generated code is a big part of that equation — more output, faster delivery, fewer bottlenecks. But AI writes code based on patterns, not context. It doesn’t know your business environment, the custom integrations built years ago, or the rules quietly holding everything together.

That missing context is where problems start. Minor mistakes attach to automated processes, compound, and ripple downstream. At enterprise scale, one update can trigger cascading effects across thousands of applications. Traditional QA simply wasn’t designed for this level of speed or interconnectedness.

Fragmentation makes it worse. Hybrid environments, multi-cloud structures, and on-prem systems running mission-critical workloads make end-to-end validation incredibly difficult. When QA and DevOps rely on manual checkpoints or siloed automation, risk hides in the seams.

Then there’s the governance question. As AI becomes more embedded in development, enterprises need intelligence woven into every step of the lifecycle. Agents that understand not just what changed, but what that change means.

Why agentic quality is the answer

Rather than treating AI-generated code as just another output to test, the Tricentis approach reframes the task entirely: the testing and governance of AI-created work needs to evolve at the same pace as the AI itself, or ideally, one step ahead.

In our newest solution, purpose-built intelligent agents take on the heavy lifting, bringing context that humans alone can’t maintain at scale. They understand application relationships, past changes, known patterns, and the constraints of enterprise environments, and they operate continuously, keeping pace with AI-driven development in a way that manual processes never could.

Tricentis solutions already help teams organize, automate, and scale their testing. Agentic quality engineering builds on that foundation, giving enterprises a guardrail system that keeps the entire software lifecycle on track without asking teams to work harder than they already are.

What the Agentic Quality Engineering Platform does

The new Tricentis Agentic Quality Engineering Platform brings together four intelligent agents and a centralized control plane called AI Workspace, which orchestrates agent behavior, governs their actions, and creates an auditable trail of decisions across the software development lifecycle.

As David Colwell put it: “AI is the wave, and if you don’t paddle fast enough, it runs you over.” Engineering teams are generating code at a scale that would have taken months just a year ago, but nobody wants to merge it without confidence it actually works. Agentic testing is the answer to the bottleneck holding back all the productivity that agentic development promises.

AI Workspace connects your existing tools — Jira, GitHub, qTest, Tosca — into custom agentic workflows. And rather than building agents yourself, the platform comes with a suite of pre-built, benchmarked, deployment-ready quality engineering agents.

The four agents designed to implement workflows are:

• Agentic Quality Intelligence monitors code changes and coverage gaps, then recommends tests to fill them
• Agentic Test Automation takes plain-language test descriptions and builds automated tests autonomously
• Agentic Test Creation analyzes requirements and generates test cases, including coverage for edge cases
• Agentic Performance Testing designs and runs load scenarios without handing off to a separate performance team

Agentic Quality Engineering Platform demo

Matt Serpone concluded the presentation by showing the value offered through our agentic platform. He showcased four key areas in the demo:

• Agentic orchestration through our AI workspace, and common quality engineering workflows
• Agentic Test Automation within Tosca, and how to use natural language prompts to generate automated test cases
• Agentic Test Creation within qTest, and how to leverage our agentic capabilities to generate test cases from analyzing a requirement
• How to build and generate custom agents within AI Workspace, which allows the flexibility to build your own agents to accommodate unique testing challenges

Watch a recording of the full launch presentation here, to see the full live demo.