Operations | Monitoring | ITSM | DevOps | Cloud

Most legacy AIOps investments underdeliver because the AI lacks context, not capability. LogicMonitor’s latest innovations expand Edwin AI’s contextual intelligence across every dimension, so recommendations are accurate, explainable, and trusted by the teams that need to act on them. Reduce incident resolution time with AI that understands your environment—not just your alerts.

LogicMonitor’s latest innovations span the entire platform to deliver the operational foundation enterprises need for Autonomous IT—complete visibility from infrastructure to end user, AI that reasons in full context, and closed-loop automation that moves from detection to resolution. Over 90% of organizations rely on at least two to three monitoring solutions—and many enterprises operate five or more.

Automated Diagnostics & Remediation reduces MTTR by closing the gap between detection, diagnosis, and resolution.

Proactive budget alerts turn cloud cost optimization into an everyday operational practice. If you are responsible for managing cloud infrastructure, you already know the pattern. Costs creep up quietly, and by the time anyone notices, it is the end of the month and you are explaining instead of preventing overruns. According to Flexera’s 2026 State of the Cloud Report, 85% of their respondents say managing cloud costs is their number one priority for the year.

Autonomous IT only works when observability gives it the context to act with confidence. On any given day, a mid-size enterprise generates tens of thousands of alerts across on-prem infrastructure, multiple clouds, SaaS tools, Internet dependencies, and AI workloads. Most of them don’t need a human. A few of them do. Telling the difference, fast enough to matter, is exactly where IT teams are losing ground.

Edwin AI’s Agent Orchestrator keeps incident investigation, context, and response aligned as work moves across tools, eliminating the manual handoffs that slow resolution. Every major incident has two timelines running in parallel. The first is the incident itself—services degrading, users affected, business impact accumulating. The second is quieter and just as costly: engineers switching tabs, re-explaining context to new responders, moving notes from one tool to another by hand.

Effective network monitoring requires path validation, not only device polling. Traditional Network Monitoring System (NMS) tools were built for static networks, not today’s hybrid reality. You poll devices, check interface counters, and still struggle to explain why users complain about latency. Traffic moves across SD-WAN architectures, cloud routing layers, and public internet paths that device metrics never capture.

Autonomous IT is the result of a long operational evolution, from static monitoring and rule-based automation to AIOps and now to systems that can increasingly diagnose, prioritize, and act within defined guardrails. Autonomous IT gets talked about like it appeared out of nowhere. As if someone flipped a switch and suddenly systems started managing themselves. The reality is far less dramatic and far more instructive. What we’re seeing today is the result of decades of incremental progress.

Fragmentation limits AI automation because context is split across systems, forcing humans to bridge the gap. Most IT environments are fragmented by design. Observability data lives in one set of systems, investigation happens in another, and execution sits behind separate tools with their own ownership and controls. During an incident, context does not move with the work.

Most operational waste comes from fragmented workflows rather than individual performance constraints. An incident begins long before any fix is applied. Alerts trigger, tickets open, and engineers start reconstructing context across systems that were never designed to operate as one. Logs, metrics, past incidents, and runbooks sit in separate tools, each requiring manual lookup, interpretation, and validation before any decision can be made.