Systems | Development | Analytics | API | Testing

Object recognition is the capability of test automation tools to identify, locate, and interact with user interface elements within an application under test. It serves as the bridge between automated test scripts and the visual elements that end users see, enabling tests to accurately simulate user actions and validate application behavior.

Your AI agent just placed 47 duplicate orders. It called the wrong API three times in a row. It looped through the same workflow for six minutes before anyone noticed. Nobody caught it in testing because nobody built the right tests. That's not a hypothetical. Enterprises using AI agents face this exact problem every week. The AI agent works perfectly in staging, but fails silently in production, and by the time the on-call engineer gets alerted, real customers are already affected.

Learn how roles, teams, workspaces, and organizations create scalable governance with Fivetran.

A financial services startup launched its AI assistant without doing a proper LLM testing checklist. Within 72 hours, it gave three customers dangerous advice, telling them to withdraw their retirement savings and invest in penny stocks. The problem? The advice was completely made up. There was no validation, no factual grounding, just confident and detailed responses that were entirely wrong. The company then spent the next six months addressing regulatory issues and rebuilding customer trust.

ClearML has announced native floating license management for NVIDIA AI Enterprise licenses with one-click deployment of NVIDIA NIM microservices across AI infrastructure. The feature, available now to ClearML enterprise customers, fundamentally changes how organizations consume NVIDIA AI Enterprise software licenses, moving from a static per-GPU assignment model to a dynamic pool that follows active workloads.

Government and defense agencies require extreme security measures to protect sensitive data like classified intelligence and military operations. Air-gapped systems, which are physically isolated from external networks, provide a robust solution by ensuring no remote access is possible. These systems are critical for deploying large language models (LLMs) safely in secure environments, enabling advanced AI capabilities like intelligence analysis and mission planning without risking data breaches.

Learn how roles, teams, workspaces, and organizations create scalable governance in Fivetran.

Organizations over the years have seen the writing on the wall: The future is cloud. Now, these companies and their DevOps teams areevolving, innovating, and pursuing new technologies, to gain a competitive edge and create new efficiencies. One of the ways they’re doing this is through application migration to cloud. In this blog, I’ll detail the nuances of application migration and how to best manage data during it, including various challenges and their solutions.

Most AI applications start the same way: wire up an LLM, stream tokens to the browser, ship. That works for simple request-response. It breaks when sessions outlast a connection, when users switch devices, or when an agent needs to hand off to a human. The cracks appear in the delivery layer, not the model. Every serious production team discovers this independently and builds their own workaround. Those workarounds don't hold once users start hitting them in production.

Test automation is essential for modern software delivery. It supports faster feedback loops, strengthens release confidence, and enables continuous integration at scale. Yet despite its importance, many teams struggle to expand automation at the pace they need. The biggest obstacle is not validating functionality. It is converting structured manual tests into actionable automation scripts. Manual tests already represent validated logic.