Systems | Development | Analytics | API | Testing

At Datadog DASH in the spring, the Tricentis NeoLoad team met folks in all types of roles – developers, test engineers, CoE leads, and SREs – and one recurring theme we found was that SREs often didn’t know much about how performance validation happens at their companies. That’s a fair division of labor. Seemingly, the performance team’s work would be related but not mission-critical to the infrastructure team’s. But that’s not entirely true.

Over the past twelve months, something has shifted in how software teams think about quality. Claude Code, Cursor, and Playwright agents have made it genuinely easy to generate functional tests without deep automation expertise. The impact is showing up across two very different kinds of teams: Both paths lead to the same place: tests accumulate without governance, you get coverage without traceability, and there’s automation debt no one owns. But coverage numbers and quality are not the same thing.

For decades, APIs have been the universal handshake of the digital economy. You want data? Call an endpoint. You want to trigger an action? POST a payload. APIs are simple, predictable, and everywhere. But in 2024, Anthropic introduced the Model Context Protocol (MCP), an open standard that dramatically changed how systems speak to one another. MCP enables AI agents to connect with external apps without custom code.

Citizens today compare government digital services to the best experiences they have in the private sector. A missed benefit payment. A tax portal that crashes during filing season. A license renewal that takes weeks instead of minutes. Today, software failures aren’t just IT problems, they’re public trust problems, and many state and local agencies are struggling to deliver digital services quickly, effectively, and consistently.

This fall, Tosca’s suite of AI capabilities will be available for on-premises deployments — giving teams the tools to meet the pressure of the AI era head-on. Customers will gain access to leading AI features, including Agentic Test Automation, AI-powered test creation, MCP connectivity, and AI Workspace, no matter their deployment type or existing infrastructure. AI’s unprecedented development means testing practices that were standard a year ago could already be outdated.

Picture a software testing failure at your company. The consequences range from money lost on weeks of rework to life-threatening catastrophe. These are the errors that keep engineers, their directors, and executives up at night. At some point, those risks came to life in the real world and pushed software teams to develop rigorous testing methodologies. But for some reason, we don’t think about data the same way. We think of it as reliable, durable, and highly unlikely to cause a big issue.

Performance engineering teams are already stretched. AI-accelerated development means more code, more releases, and more pressure on the people responsible for making sure it all holds up. Tricentis NeoLoad 2026.2 is built around a straightforward premise: performance validation must scale to match the pace of delivery, and that means more than just making specialists faster.

Pre-built Oracle test cases have become an increasingly popular resource as organizations look for faster ways to scale test automation. The appeal is understandable: reusable assets and templates can help teams accelerate onboarding and reduce the effort required to begin automation initiatives. Tricentis offers an Oracle test case library as a reference framework rather than an instant, maintenance-free automation solution.

AI is transforming industries at an unprecedented pace. Boards are demanding AI roadmaps, CTOs are accelerating digital transformation, and each of those initiative sits on the same foundation: data. Here’s an uncomfortable truth: many enterprises can’t tell you whether that foundation is solid. They hope and maybe even assume it is, but they can’t be certain if they aren’t testing it.

At Sapphire 2026, SAP introduced its latest vision for customers: the “Autonomous Enterprise,” a new model of business where AI agents run core ERP processes, from HR to finance and beyond. Reimagining the modern SAP enterprise also means reimagining its tools. At this year’s event, SAP renamed the Integrated Toolchain to the SAP Agent-led toolchain, revamping its role for a more intelligent enterprise.