Operations | Monitoring | ITSM | DevOps | Cloud

AI observability closes the gap between “something’s wrong” and “here’s what to fix.” If you run AI in production, you might have felt the whiplash. Yesterday, your LLM answered in 300 milliseconds (ms). Today p99 crawls, costs spike, and nobody’s sure if the culprit is model behavior, data freshness, or GPUs stuck at the ceiling. Dashboards light up, but they don’t tell you which issue puts customers at risk. That’s the gap AI observability closes.

AI workloads break every assumption you have about infrastructure management. AI is everywhere. Machine learning-based tools are answering customer service questions, accelerating incident resolution, catching fraudulent transactions, spotting defects on production lines, and powering late-night searches that delve into the random topic that pops into your head right before bedtime. Behind every prediction, response, or generated sentence is massive computing power doing serious, continuous work.

Monitoring AI systems isn’t business as usual. Monitoring AI isn’t like monitoring traditional systems. You can’t just track uptime or response times and call it a day. AI models evolve, data shifts, and behavior drifts over time, which means your monitoring has to evolve, too. If you’re running AI workloads in production, you already know this. Your models might look healthy according to your infrastructure metrics, but they’re still making bad predictions.

Artificial intelligence (AI) infrastructure requires four pillars working in tandem as a system (compute, storage, networking, and orchestration) tailored to your actual workload needs, not hype. Artificial intelligence (AI) infrastructure isn’t just more hardware. It’s a new class of system—highly distributed, resource-intensive, and tightly coupled across compute, storage, and network layers.

In today’s digital landscape, application performance isn’t just about monitoring several key apps and “keeping the lights on,” it’s about understanding the full breadth of your interconnected business services and ensuring you’re delivering seamless, reliable experiences to customers and teams alike. But as applications grow increasingly distributed across cloud, on-prem, and hybrid environments, monitoring them holistically can become a serious challenge.

Edge computing has been the buzzword of the decade. Everyone is talking about pushing intelligence closer to the edge, but most of that intelligence still needs a map and a flashlight. This week, CRN named LogicMonitor to its 2025 Edge Computing 100, recognizing companies that are actually doing something useful at the edge instead of just hyping it. We are honored. We are also a little amused.

At LogicMonitor’s Senior Leadership Team Offsite in July, I sat down for a candid conversation with Kyle Hill, CTO of ANS Group. As a longtime LogicMonitor customer and leader of a 700+ person tech powerhouse, Kyle offered sharp insights into scaling infrastructure, unlocking AI-driven value, and what true partnership looks like in today’s MSP world. Here’s an edited and condensed version of our conversation.

Recommendation: Focus your next AI initiative on one high-impact workflow. Measure, iterate, and scale. Agentic AI has quickly become the next frontier of enterprise automation. Instead of static AI tools that wait for human prompts, agents act on behalf of users by autonomously reasoning, sequencing steps, and taking action within defined guardrails.