Operations | Monitoring | ITSM | DevOps | Cloud

Agentic AI Essentials: Adoption Pitfalls and How to Avoid Them

In the last article in this series, we explored how IT professionals and leaders can cut through the hype surrounding agentic AI and gain a deeper understanding of what the technology actually offers. Now, we turn to the practical side: how to integrate it effectively. Let’s explore the challenges and outline strategies that organizations of all sizes can use to adopt agentic AI with confidence.

Why Your Hotel's Review Responses Matter More Than You Think for Guest Loyalty

Price wars? Those are yesterday's battles. Location advantages? Sure, they help. But here's what really determines whether guests come back to your hotel: trust. And trust doesn't live on your homepage; it lives in your review section. Every time someone takes fifteen minutes out of their day to write about their stay, your reply (or radio silence) tells them exactly who you are as a brand.

Observability for GenAI Applications (Grafana OpenTelemetry Community Call)

In this episode, we’re diving into observability for Generative AI apps. AI helps us write code and monitor applications in production - but how do we observe the AI itself? And how do we make sense of complex, non-deterministic AI systems? We’re joined by two great guests: Ishan Jain, working on GenAI observability and Luccas Quadros, working on Grafana Assistant. Together, they bring both platform-level insights and real-world perspectives.

From idea to agent: Building AI workflows with relaxAI and n8n

Join us for this live online webinar as we explore how to design, build, and deploy practical AI agents using n8n’s workflow automation platform powered by relaxAI’s UK sovereign infrastructure. Our speaker, Ben Norris, AI Engineer at Civo, will guide you through the real-world process of creating intelligent agents that automate tasks across tools and services, all without deep coding expertise.

[Webinar] Building Quality-Driven Agentic AI in Noisy Big Data Environments

Watch as Itiel Shwartz, Komodor CTO and Co-Founder as he shares hard-won lessons from developing an AI agent that processes millions of K8s events daily to deliver autonomous troubleshooting that reached 95%+ accuracy in benchmarking. This webinar covers: Building production ready systems that maintain reliability when 90% of your data is noise. How Komodor developed an AI SRE agent that processes millions of K8s events daily to deliver autonomous troubleshooting that reached 95%+ accuracy in benchmarking.

An introduction to GPU time-slicing

GPUs are no longer a niche component. Gamers know them for immersive graphics, workstation users rely on them for balanced performance, and in the age of AI, GPUs have become one of the most in-demand resources in modern infrastructure. They are also expensive. That reality creates two immediate constraints, for individuals and enterprises alike: GPU-backed instances should be provisioned deliberately, and once provisioned, they should be used efficiently.

AI Anomaly Detection: Catch AI Cost Surprises Before They Kill Margins

Consider this: traditional cloud cost monitoring was like checking your fuel gauge once a month — after the trip was already over. That model worked when infrastructure scaled slowly. You provisioned resources predictably and paid for stable, linear usage. AI breaks that model. Today, AI costs behave like a high-performance engine with a hypersensitive throttle. A small input, like a prompt change or a single power user, can dramatically increase your fuel burn in seconds.

Measuring Claude Code ROI and Adoption in Honeycomb

At Honeycomb, we’ve been using Claude Code across our engineering team for a while. Anecdotally, I had a sense of who the power users were, and I had seen some examples of complex usage. But I wanted to be able to confidently answer questions, like: Claude Code supports OpenTelemetry out of the box, which means sending telemetry to Honeycomb takes just a few minutes of configuration.