Systems | Development | Analytics | API | Testing

For the first time ever, artificial intelligence has topped NASCIO’s annual ranking of state CIO priorities, ending cybersecurity’s 12-year reign at number one. It’s easy to see why: public agencies – like their private sector counterparts – see real potential in AI to modernize operations, accelerate service delivery, and help lean teams do more with fewer resources.

Software runs mission delivery in the public sector. As agencies modernize and systems grow more connected, the cost of a failed release climbs. The result can be service disruptions, compliance gaps, and loss of public trust. Automated testing is central to managing that risk, but it raises a practical question: which approach is right? Two options come up often: robotic process automation (RPA) and model-based testing (MBT). Both support automation, but they were built for very different purposes.

Picture a large-scale grocery and retail chain launching its first online storefront. The setup is complicated: a single online order must travel through an SAP eCommerce platform, multiple payment and loyalty systems, before disappearing into back-end SAP supply chain and ERP apps. The complexity of merging the online and offline business is like a “digital tsunami.” For many SAP enterprises, this sounds familiar.

Quick summary: Performance teams can generate tests in minutes, but the analysis still takes hours. Agentic Performance Testing in NeoLoad uses domain-specialized AI agents to deliver a finished analysis from a single request, so engineers can start with the conclusions, rather than the raw data. Performance testing answers a critical question in the quality engineering lifecycle: will this hold up when real people use it, under real conditions, at real volume?