Operations | Monitoring | ITSM | DevOps | Cloud

Agentic AI is making waves across various industries, and its potential to transform ITSM is undeniable. With all the talk about agentic AI capabilities, you are probably daydreaming about streamlining ITSM processes through automated incident management, automated change risk analysis, improved efficiency through service desk automation, or enhanced service quality with predictive maintenance. But you also may start to wonder: How real is all of this? Is my organization truly ready for agentic AI?

There’s a certain comfort in the glow of your network operations center (NOC) dashboards. For some time, the sign of a well-run NOC was that sprawling bank of screens, each dedicated to a different domain. One for the WAN, showing link status. Another for the data center, tracking backbone health. A third for cloud consumption, pulling metrics from your provider. Each screen is a neatly bordered kingdom, diligently monitored by its own set of tools. As long as the lights are green, all is well.

As enterprises scale in complexity, the infrastructure landscape is no longer just cloud or on-premises, it’s both. Hybrid is the new normal and it’s here to stay. And with that shift comes a new demand: a unified, scalable observability solution that works across the entire tech stack, from legacy hardware to cloud-native workloads. That’s where LogicMonitor comes in.

As large language models (LLMs) grow more powerful, organizations are deploying agentic AI applications to tackle complex, multi-step tasks. With Amazon Bedrock Agents, developers can orchestrate these agents to manage tasks such as triggering serverless functions, calling APIs, accessing knowledge bases, and maintaining contextual conversations—all while breaking down complex user requests or tasks into manageable steps.

Kentik transforms real-time network telemetry into actionable alerts for AI-optimized data centers. By converting database queries into custom alerts, engineers can detect issues like elephant flows, idle links, and packet loss before performance suffers and triggers alerts in systems like ServiceNow or PagerDuty.

Production AI needs a network that can keep up. Learn why private, scalable connectivity is the key in our webinar recap with Vultr. AI is no longer a proof-of-concept hiding in a developer lab. It’s a full-fledged production workload, and it’s hungry for data. But as enterprises move their AI strategies from theory to reality, they’re hitting a wall that isn’t about algorithms or processing power – it’s about the network.

Now available: Secure, real-time access to your observability data via Coralogix’s Model Context Protocol (MCP) Server. AI agents are only as powerful as the context they’re given. Today, we’re excited to announce the launch of the Coralogix MCP Server, which enables third-party AI agents to connect directly to your observability data across production, staging, and other environments.