Operations | Monitoring | ITSM | DevOps | Cloud

When I spoke at AWS London earlier this year, I had the chance to discuss something that more and more teams are starting to feel: traditional observability doesn’t cut it for AI systems. In AI, “Is it running?” is no longer enough. We have to ask, “Is it right?” When I delivered that line, I saw the heads nodding. Everyone’s excited to build with LLMs, but when it comes to actually monitoring them in production? That’s where things fall apart.

ScienceLogic’s vision for IT operations isn’t just about improving tools—it’s about changing the entire paradigm, flipping your day-to-day upside down. We’re moving beyond dashboards and alerts, beyond human-only workflows and rules-based systems. The future is autonomous. It’s intelligent. It’s agentic. And it’s already being realized through the power of Skylar AI.

As organizations rely more on complex IT systems and cloud-based services, keeping everything running smoothly — and reliably — has become a top priority. That’s where IT service performance monitoring comes in, giving teams the visibility they need to make sure systems stay healthy and responsive. By tracking a range of technical and user-focused metrics, businesses can quickly identify and address issues before they impact operations or end users.

Many organizations don’t realize how important it is to monitor how their applications run in production. Without Application Performance Monitoring (APM), it becomes difficult to detect and resolve issues quickly, leading to increased downtime, wasted developer effort, and poor user experience. These hidden costs, though not always visible at first, can impact customer satisfaction, reduce team efficiency, and result in lost revenue.

Some people can get an AI assistant to write a day’s worth of useful code in ten minutes. Others among us can only watch it crank out hundreds of lines of crap that never works. What’s the difference? There are some skills specific to AI development. There are also properties of the codebase we’re working in that make it amenable to AI assistance. Most AI demos use projects created from scratch with AI in mind—cute.

We optimized log processing pipeline by moving from sequential to concurrent processing at the entry level, achieving 30% higher throughput and better resource utilization without increasing infrastructure costs. When customers start sending millions of logs per minute, you quickly discover whether your processing pipeline can actually scale with vertical scaling.