Systems | Development | Analytics | API | Testing

Tricentis’ VP of Products explains what’s next for Tosca 2026 has been a turning point for software testing, and for Tosca in particular. In this conversation, Magda Neagu, Lead Product Marketing Manager for Tosca, talks with David Landsberg, Tricentis’ VP of Products, about where Tosca is headed, how agentic AI is changing quality engineering, and what the testing world could look like a year from now.

In this AI transformation roundtable, Nate Custer, Principal Technologist at TTC Global, shares how to measure productivity on an agentic quality engineering team. Drawing on a mainframe regression story, he explains why saving time only matters if you use it to close coverage gaps. He also argues that QA teams should track how often they have to fix AI-generated code, and why that measurement might mean more than speed or headcount.

Someone opens a spreadsheet, pastes data from two systems side by side, and compares row by row, hoping to catch every error before it reaches production. In conversations with enterprises, nine out of ten still test data this way. In this short video, Adnan Ćosić, Senior Product Marketing Manager at Tricentis, explains why "stare and compare" and custom scripts can't keep up with modern data landscapes, and how Tricentis Data Integrity automates data testing end to end so you can trust every decision.

Requirements change in Jira all the time, and QE teams have to catch every edit to update their test cases. Connect Tricentis AI Workspace agents to Jira and qTest, so any requirement change generates and updates test cases for you with the right context. In this walkthrough, Senior VP of Product Marketing Ryan Yackel, will show you how a Jira edit triggers an agentic workflow, pushes test requirements into qTest, and creates test cases with a human-in-the-loop checkpoint.

Prompting your agentic IDE to write tests gives you different steps and different results every time. The result is inconsistent and low-quality test cases. Combine AI Workspace's agentic workflows with your Cursor IDE to generate a consistent, reusable test base straight from your code repository. In this walkthrough, Product Marketing SVP Ryan Yackel will show how Tricentis generates test cases from a GitHub repo, stores them in qTest, and runs tests against a desktop application.

Modern applications move faster than traditional testing can keep up with. Teams inherit apps they did not build, ship on tight timelines, and release AI-generated code that no one has fully tested, so the gap between what gets built and what gets verified keeps growing.

Going from a written requirement to an executable test usually means writing the steps and data yourself, then mapping each one to the application by hand. Pair Tricentis AI Workspace's requirement analysis with Tosca's agentic automation to go from a Jira ticket to a running, reusable test case for your SAP Fiori applications. In this walkthrough, you will see how to generate a test case from a requirement, map it to the Fiori UI, and save it for regression.

Agents are easy to build and hard to measure. When your company runs hundreds or thousands of them, hand-written evals can’t keep up, and it’s unclear which agents are correct, efficient, and ready for users. In this conversation, Ryan Yackel, SVP of Product Marketing, and Andrew Demers, Product Manager for Agent Score at Tricentis Labs, explain how to measure agent quality, score agents automatically, and trace issues back to behavior.

Most AI tools are designed as one assistant or co-pilot answering one prompt, but Tricentis Agentic Performance Testing is built differently. In this video, Product Marketing Manager Twan Koot explains the two pieces behind it: purpose-built tools and a team of seven specialized agents, each designed to do a performance tester’s job the way it should be done. Key highlights.