Tricentis has extended a cloud-based testing service to make it easier to ensure that test cases are designed to continuously validate business workflows against mission-critical business objectives, not just technical functionalities.
In addition, the latest version of Tricentis Tosca also makes it simpler to make use of reusable test step blocks using a set of elastic execution agents that enable DevOps teams to consume infrastructure resources more efficiently at a time when there is greater sensitivity to the total cost of IT.
At the same time, Tricentis has revealed it plans to add a set of generative artificial intelligence (AI) agents to further automate testing tasks. Previously, Tricentis has made available a set of AI co-pilot tools across its test automation platforms, but with the rise of agentic AI the goal will soon be finding a way to offload tasks to a series of AI agents that are managed via the Tricentis platform.
Adnan Ćosić, a senior product marketing manager for Tricentis, said the overall goal is to make it simpler for DevOps teams to cost effectively create the right test for the right use case using a combination of agents and Business Flow Designer, a graphical tool that provides no-code approach to validating business workflows and their data dependencies. DevOps teams can also employ a set of dashboards that have been added to surface real-time insights into metric and actual test execution progress.
Tricentis Tosca already leverages machine learning algorithms to automate the management and provisioning of test data to reduce the level of manual effort that would otherwise be required. As the volume of code being created continues to exponentially increase in the age of generative AI, the need to find ways to automate the testing of applications will become more acute, noted Ćosić. In fact, some organizations might soon find themselves building and deploying more applications in the months ahead than they have in the last several years combined.
It’s not clear to what degree organizations are relying on dedicated application testing teams as more responsibility for the quality of the applications being developed has in recent years shifted further left toward application developers. However, in theory as application developers discover and remediate more issues on their own, application testing teams should gain the time needed to conduct more rigorous tests before applications are deployed in production environments.
Regardless of approach, however, it’s clear that as application testing becomes more automated it should become feasible to run more tests. One of the issues that many organizations still contend with is the simple fact that as application delivery deadlines approach, one of the ways that lost ground is made up is by reducing the number of tests that might otherwise be conducted.
Hopefully, as AI is more broadly applied to testing, the overall quality of the applications being deployed will continue to steadily improve. In the meantime, each DevOps team should be reviewing which testing tasks might soon be offloaded to, for example, an AI agent. After all, many testing tasks are tedious, which may account for why they are often not as thoroughly conducted as everyone might otherwise like.
