Operations | Monitoring | ITSM | DevOps | Cloud

Versatile Automation: Applications of AI Across Different Sectors

From small and medium-sized enterprises to larger corporations, virtually all industries are asking their staff to work faster, do more with less, and keep up with an ever-increasing amount of work, accelerated timelines, repetitive or manual tasks, complex systems and data-intensive workloads in the digital age. The result? Oftentimes higher profits, but with greater risks of stress, frustration, and even lower quality customer service.

Get Third-Party Outage Alerts in Discord with StatusGator

When SaaS tools go down, teams need fast, reliable alerts right where they communicate. Now, with the StatusGator integration for Discord, you can receive real-time third-party outage alerts directly in your server. Whether you’re monitoring the status of AWS, Slack, GitHub, or Google Workspace, StatusGator keeps your team informed instantly when disruptions happen.

From Firefighting to Proactive Resolution: How Almaden's Collective IQ Is Revolutionizing the Service Desk

Picture this: It’s Monday morning, the Service Desk ticket queue is piling up, and Level 1 analysts are already in full “firefighter mode.” Every ticket feels like a blaze: users locked out, critical apps running slow, unexplained system failures. The problem? They’re trying to put out flames without seeing where the fire started. Without real visibility into the environment, troubleshooting is based on trial and error.

Observability-as-Code: Bring synthetic monitoring into your pipeline

Your team just deployed to production. The infrastructure spun up in 90 seconds, but recreating your monitoring? That’ll take hours. It’s added late in the process, managed through dashboards, and prone to inconsistency. Short-term, this slows delivery and creates visibility gaps that surface only during incidents. Long-term, it leaves a business-critical capability out of your observability pipeline.

Signal Enrichment: Turning Noisy Alerts into Actionable Intelligence

This is the fourth post in our series on the future of incident management, which builds upon The Future of Incident Management: Your Blueprint for Operational Excellence, How Native Process Automation and Auto-Remediation Drive Operational Excellence, and Service Intelligence is the Future of Proactive Incident Management.

Automated RAG pipeline evaluation and benchmarking with RAGAS

Retrieval-Augmented Generation (RAG) pipelines have become an integral part of how Large Language Models (LLMs) access information beyond their training cutoff. These pipelines enable LLMs to deliver current, accurate, and grounded responses. By fetching relevant external documents, RAG mitigates common LLM challenges like factual inaccuracies and hallucinations. However, this methodology introduces a new complexity: evaluating RAG pipeline performance is particularly challenging.

The observability maturity curve: How IT leaders are shifting from tools to outcomes

Observability has come a long way from its origins in monitoring logs and metrics. Today, it sits on a maturity curve: Organizations move from fragmented tool stacks to unified platforms to proactive engineering practices that tie reliability to business outcomes. To better understand where IT leaders are on this curve, Grafana Labs surveyed 150 decision-makers across industries in advance of ObservabilityCON 2025.

How to automate sending SquaredUp dashboards to Slack with the Notification API

SquaredUp's existing notifications fire when monitors change state. With Notification API, you control the trigger. Send dashboards on a schedule, before meetings, or on-demand through chat commands. In this step-by-step guide, you’ll learn how to automate sending SquaredUp dashboards to Slack. I’ll use Power Automate as the example, but the same approach works with other automation tools such as Zapier, Make, n8n, or even a custom script, as long as it can send an HTTP request.

LLM Observability Explained: Prevent Hallucinations, Manage Drift, Control Costs

Large Language Models (LLMs) are transforming how businesses interact with users, automate workflows, and deliver insights in real time. But as powerful as these models are, running them at scale comes with unique challenges, from hallucinations and latency spikes to cost overruns and user trust issues.