Operations | Monitoring | ITSM | DevOps | Cloud

A step-by-step look at how Edwin AI uses native LogicMonitor logs, topology, and context to turn root cause analysis from alert-driven inference into evidence-backed investigation. Most root cause analysis today starts with alerts and ends with explanations that sound reasonable but can’t be verified. An alert is fed into a language model, and the output looks like an answer. It often isn’t.

ITOps teams can achieve cost management of AI workloads with an observability platform that connects AI usage and performance with cloud spend for clear visibility and predictability. Behind the buzz around artificial intelligence, or AI, many companies are discovering the hidden and compounding costs of AI adoption.

LogicMonitor delivers AI cost optimization by unifying infrastructure telemetry, AI-specific signals, and cloud financial data into a single workflow, so teams can move from visibility to continuous, operationalized cost control. In Cost Optimization for AI Workloads: From Visibility to Control, we explored why AI workloads introduce new layers of cost complexity—from GPU-heavy compute and token-based pricing to distributed infrastructure that obscures true spend.

Service reliability has outgrown uptime checks and component-level tools, creating friction that slows response, increases toil, and wears teams down. Uptime checks can pass, high availability can be in place, and users still can’t complete basic actions. Pages load slowly, latency spikes, and requests stall — all without a single system flagged as down. Availability measures whether a service is running.

Hybrid and multi-cloud monitoring breaks down when teams can’t connect signals to customer impact fast enough to act. Hybrid and multi-cloud sound simple: run some workloads in public cloud, keep some on-premises, and connect it all. But in practice, you’re managing dependencies across teams and systems, tools that don’t share context, and incidents that refuse to stay in one place.

Agentic, autonomous ITOps improves incident response by carrying context from detection through resolution, reducing noise, delay, and manual coordination. Most IT incidents don’t fail due to missing data. Monitoring systems generate more than enough signals. The problem is that understanding those signals—and deciding what to do with them—happens in fragments. Engineers move between dashboards, logs, tickets, and chat threads, stitching together context by hand.

This article kicks off a 4-part series on leveraging AIOps to provide a more efficient, cost- and resource-saving, reliable, and agile IT infrastructure.

The future of ITOps automation is better control over what AI agents can see, share, and do. AI automation in ITOps is expected to resolve incidents, reduce operational load, and operate with limited human involvement. Those outcomes depend on systems that can take action, not just surface insight. Agentic AI enables that shift. AI agents can correlate signals across tools, update tickets, trigger remediation, and coordinate workflows without waiting for instruction.

IT teams collect extensive monitoring data but struggle to turn it into fast, confident decisions during incidents. Most IT leaders aren’t worried about whether their environments are monitored—they’re worried about whether their teams can make sense of what they’re seeing quickly enough to actually resolve issues. When something breaks, the problem usually isn’t finding data. Dashboards show activity, alerts indicate changes, and logs capture events across the entire stack.

Prompt engineering improves single responses, but agent performance is determined by how execution context is captured, replayed, and constrained over time. For the past few years, enterprises have obsessed over prompts, with entire roles emerging around their design and an ecosystem of tooling and templates following close behind. This focus delivered early gains because it allowed teams to rapidly improve outputs without modifying the surrounding system. Over time, those gains flattened.