Operations | Monitoring | ITSM | DevOps | Cloud

Financial services AI demands more than models and prompts. Context engineering provides real-time, governed, and explainable intelligence with Elastic serving as the foundational context layer. Artificial intelligence in financial services is no longer constrained by model capability. The real bottleneck is context.

For much of the past decade, many of the most ambitious ideas in artificial intelligence lived primarily in research papers, labs, and long-term roadmaps. Agentic AI was no exception. The concept of AI systems capable of reasoning, planning, and acting autonomously was widely discussed but largely theoretical. But earlier this month, Gartner released its report The Future of NetOps Is Agentic, reflecting a growing consensus that this has changed. What was once conceptual is now becoming operational.

Every application tells a story of user intent, system behavior, and business impact. To truly understand how your application performs, you need to go beyond logs and errors. You need metrics that provide actionable visibility across your stack. Application performance metrics are the foundation for delivering high-quality digital experiences, and they empower DevOps teams, developers, engineers, and site reliability engineers (SREs) to respond faster, scale smarter, and continuously improve.

As part of our day-to-day consulting work at GripMatix, we spend a significant amount of time in various customer environments where we are designing, validating, and troubleshooting EUC platforms. This particular issue surfaced during work for one of our customers, where we were validating Ivanti Workspace Control (IWC) on a new Windows Server 2025 environment.

Top Tips is a weekly column where we highlight what’s trending in the tech world today and list ways to explore these trends. This week, we’re looking at why interpretation matters when messages, meetings, and notifications never seem to stop. We live in a world where messages travel faster than meaning. Emails are sent in seconds, chats stack up by the hour, and meetings are recorded, transcribed, and summarized before we’ve had time to process what was actually said.

IT Operations Leaders, Platform Engineering Managers, SRE Team Leads, and DevOps Directors managing complex, multi-tool observability environments who are struggling with alert overload and extended incident resolution times.

Digital businesses today generate a flood of telemetry—metrics, logs, traces, and events—at a scale that grows exponentially with every new application, cloud service, and user interaction. In one recent IDC survey, every organization reported sharing observability data across teams, yet nearly half said poor collaboration still prevents them from identifying performance problems.