AI-driven automation for IT resilience

Reduce MTTR and IT spend by automating key operations at every level of your IT stack with OpManager Nexus (formerly OpManager Plus)

Overview

OpManager Nexus can detect critical issues anywhere in your IT stack, deliver them as context-rich alarms across multiple platforms, and drive complex remediation steps by coordinating with different solutions in your IT tool kit. Optimize workforce productivity, improve MTTD and MTTR, and realize 24x7 availability with a self-healing IT stack powered by OpManager Nexus.

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Map IT issues into business impacts

  • Organization maps generate a visual hierarchy of your IT systems across different layers and keeps it automatically updated for changes in topology.
  • Keep tabs on your infrastructure and the roles they play in your infrastructure with minimal manual effort.
  • Translate network-level issues into the application and service-level incidents and vice-versa to perform faster root-cause analysis.

Transform KPIs into actionable insights

  • ManageEngine’s versatile in house AI solution, Zia, automates diagnostics and generates insights to simplify IT management.
  • Chat with Zia to get summaries and insights delivered about your monitored IT environment.
  • Track the utilization trends of your critical IT resources and predict the time remaining before they run out to simplify provisioning.

Automate performance baselines and alerts

  • Replace traditional static alarms with dynamic, AI-calculated baselines that predict expected performance every hour and creates alarms for violations.
  • Reduce false positives by ignoring "normal" performance spikes and Identify subtle deviations that might stay below static threshold levels
  • Automatically adjust alarms for peak business hours and maintenance windows and eliminate the need for admins to manually set thousands of thresholds across a hybrid stacks.

Correlate service outage impacts and root cause

  • Unify disparate alarms into structured, high-level incidents. These are enriched with deep impact context across your network, IT infrastructure, and application layers for better visibility.
  • Utilize historical alarm datasets to pinpoint recurring alert sequences. When familiar event flows emerge, they are automatically consolidated into actionable, multi-layered problems.
  • Securely transmit real-time telemetry to Generative AI LLMs via OpManager Nexus' MCP server. This allows you to centralize data from various IT tools into unified dashboards and task LLMs with automating complex incident responses.

Automate incident response

  • Automatically execute complex, multi-step workflows when specific network conditions are met with a code-free, drag-and-drop workflow builder.
  • Reduce the time required to restore services by automating incident response.
  • Ensure that every incident follows the exact "Standard Operating Procedure" (SOP), eliminating human error.

Strengthen operational resilience

  • Automate config deployment, rollbacks, and backups. Leverage syslog-based config change detection to prevent config drift.
  • Deploy programmable configlets to automatically implement config changes securely across multiple devices.
  • Create visual workflows to monitor, detect, and resolve compliance violations at the same time.

Use Case Scenarios

FAQ

What is AIOps?

AIOps, or Artificial Intelligence for IT Operations, represents the strategic deployment of AI and Machine Learning (ML) to transform IT infrastructure management. These technologies optimize IT workflows by automating the processing of massive data streams characterized by high volume, velocity, and variety.

Typically, AIOps frameworks leverage ML-driven pattern recognition, noise suppression, and automated orchestration to support IT personnel. Next-generation AIOps platforms also integrate Generative AI (GenAI) and Large Language Models (LLMs) to further streamline and simplify operational complexity.

What is the difference between AI, ML, and AIOps?
  • Artificial Intelligence (AI): An expansive field encompassing all technologies that enable machines to simulate human cognitive functions and perform complex tasks.
  • Machine Learning (ML): A specialized branch of AI that utilizes advanced algorithms to parse vast datasets. It enables systems to improve their behavior and make data-driven predictions without explicit, rule-based programming.
  • AIOps: The practical implementation of AI and ML within the domain of IT operations. It relies on robust monitoring architectures to ingest high-velocity data, including:
  • Logs: Raw, unstructured data from applications and servers.
  • Metrics: Time-series telemetry (e.g., CPU load, memory usage, latency).
  • Traces: End-to-end maps of requests moving through microservices.
  • Events: Specific, discrete notifications from monitoring utilities.

AIOps tools focus on distilling performance metrics, filtering out irrelevant alerts, and automating routine operational tasks.

Are AIOps and MLOps the same?

No. While AIOps focuses on embedding AI/ML into IT Operations (ITOps) to support IT administrators, SREs, and network engineers, MLOps refers to the operational framework used to build, deploy, and manage AI models reliably. MLOps is designed for the needs of data scientists, AI engineers, and DevOps teams.

What is an AIOps platform?

These are comprehensive IT solutions that offer AIOps capabilities as a primary feature set. A standard AIOps platform includes:

  • Telemetry Engine: To poll and aggregate data across the infrastructure.
  • ML Analytics Engine: To interpret and find meaning in the collected data.
  • Visualization Layer: Dashboards and reports that provide contextual insights.
  • Automation Suite: Tools to reduce manual intervention and repetitive labor.
  • Reinforcement Loop: A feedback mechanism to refine model accuracy over time.
What are the benefits of AIOps in IT monitoring?

AIOps delivers several critical improvements to network oversight:

  • Reduced MTTD (Mean-Time-to-Detect): Automated analysis identifies anomalies far more rapidly than manual human intervention.
  • Suppression of Alert Fatigue: By filtering out redundant noise, AIOps ensures teams focus only on high-priority notifications.
  • Higher Detection Accuracy: By adapting to shifting network baselines, AIOps minimizes false positives.
  • Accelerated Root-Cause Analysis: Tools assist operators in quickly identifying the underlying source of network disruptions.
  • Optimized MTTR (Mean-Time-to-Resolve): The combination of rapid detection, clear diagnostics, and automated responses allows teams to restore services and peak performance with minimal delay.
What sets OpManager apart from other AIOps tools?

OpManager can provide you with a unified view across your IT stack, from physical hardware to complex cloud microservices. By breaking down data silos, you can correlate performance metrics across networks, servers, and applications. This full-stack approach ensures that your team isn't just seeing individual alerts, but is understanding the health of your entire digital ecosystem, allowing you to identify and resolve performance bottlenecks at any layer of the stack instantly.

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