Operations | Monitoring | ITSM | DevOps | Cloud

AI is transforming how infrastructure and platform teams design, deploy, and operate systems. As organizations move from experimentation to production, a clear pattern is emerging. AI can decide what should change, but it cannot safely control how those changes are executed. This creates a gap in modern architectures. That gap is filled by a control plane. That control plane already exists in Puppet Enterprise Advanced.

Getting your application deployed feels like finishing a race. You push the code, the containers spin up, the health checks go green, and for a brief moment everything feels solved. Then Day 2 arrives. Day 2 is not a specific date. It is the entire operational life of your application after that first successful deployment. It is the stretch of time that can last years, and it is where most teams quietly discover that deployment was the easy part.

AI Observability is a Critical Priority for ITOps Teams See how LogicMonitor helps ITOps teams monitor AI workloads, reduce blind spots, and move toward Autonomous IT. Schedule a meeting AI has shifted from experimental pilots to everyday business operations. Customers are interacting with AI-powered applications. Engineering teams are building with LLMs, GPUs, APIs, and automation at a much faster pace. That adds to the visibility strain on already overburdened ITOps teams.

The Scout MCP server connects your AI assistant directly to your Scout Monitoring data. Instead of switching between your editor, Scout, and a chat window, your assistant can pull traces, errors, N+1 insights, and endpoint metrics on its own and use that context to suggest or make fixes right in your codebase. This covers how to connect it, what to ask it, how other teams are using it, and what we shipped recently.

Yesterday’s leak of a BGP hijack intended to block Telegram in India is the latest routing mishap best described as intentional, but also accidental — a pattern dating back to Pakistan Telecom’s infamous hijack of YouTube in 2008, in which a domestic block escaped containment and disrupted the service worldwide.

Most security vendors hide their documentation behind a login. Some don’t write it at all. You get a sales page, a demo, and a request to install an agent on your servers, and you’re expected to trust that the thing does what the marketing says. That’s backwards. So we wrote the docs, and we put all of them at certkit.io/docs. No login, no account gate, no “contact us for details.” You can read every page before you create an account.

The chaos of manual on-call management is a familiar story for many IT Operations teams: frantic phone calls, confusing spreadsheets, missed alerts, and frustrated engineers on the verge of burnout. This reactive approach doesn’t just strain your team; it risks service-level agreement (SLA) breaches and customer churn.

The impact vision for PagerDuty.org is to enable mission-driven teams to build a resilient world and a sustainable future for all. As a leader in modern, AI-first operations, we know that operational excellence supercharges social impact. As artificial intelligence rapidly reshapes the social sector, this commitment to resilience and efficiency has never been more vital.

When a CI pipeline runs on cloud infrastructure, the build machine is ephemeral. It spins up, executes your build, and disappears. During that window, you have zero visibility into how much CPU and memory your pipeline actually consumes. This blind spot creates real problems. Teams over-provision VMs "just in case," wasting compute spend. Others under-provision and deal with silent OOM-kills or CPU throttling — the only clue being a cryptic exit code 137.

The shift to autonomous and AI-assisted security operations is well underway. But there is a quiet infrastructure decision that will determine whether your AI agents are sharp or sluggish: how your security data is structured before it ever reaches them.