Operations | Monitoring | ITSM | DevOps | Cloud

Harness AI + MCP server: A Single Prompt to Accelerate the Software Development Lifecycle

Pipeline Creation: Using a single prompt in the IDE, a CI/CD pipeline is created and triggered via the agent connected to the Harness MCP server. Failure Diagnosis and Fix: When the pipeline fails, the agent is used to diagnose the issue (a failed dependency) and propose a fix, which is then committed, pushed, and the pipeline re-triggered to succeed. Deployment: After a successful build, the artifact is deployed into a Kubernetes cluster. Incident Response.

How Autonomous Are Your IT Operations, Really?

This post introduces a six-level maturity model that defines what true autonomy looks like in IT operations, from basic AI chat interfaces to fully coordinated agent ecosystems. ITOps teams have more automation tooling than ever, and yet incident response still depends heavily on human judgment to hold it together. Alerts fire, engineers dig through dashboards, context gets assembled by hand, and someone at the end of the workflow makes the final call.

What is Agentic Observability?

Agentic observability is the instrumentation and correlation needed to explain and control agent behavior across multi-step workflows. Legacy observability focuses on runtime health and service behavior. You monitor metrics like CPU usage, memory, latency, and error rates to confirm that applications and infrastructure are functioning as expected. When a workflow degrades, the proximate cause is often a crash, timeout, permission error, or resource constraint.

GPU Fragmentation Is Killing AI Economics

By 2026, the GPU shortage isn’t a supply-chain hiccup anymore. It’s baked into the system. Even after pouring billions into CapEx, most enterprises still want 40% more GPU capacity than they actually have. And it’s not because they’re chasing moonshots. Technology companies are training foundation models while serving inference for millions of users on the same clusters. AI labs are juggling fine-tuning, evaluation, and real-time experimentation side by side.

Top 12 AI and LLM Observability Tools in 2026 Compared: Open-Source and Paid

Artificial intelligence has moved far beyond experimentation. In 2026, AI systems are embedded into customer support workflows, clinical decision support tools, fraud detection engines, and internal copilots across nearly every industry. Adoption is accelerating quickly. According to McKinsey, 23% of organizations are already scaling agentic AI systems, while another 39% are actively experimenting with them. Yet the path to reliable production AI remains uncertain.

Webinar recap: FinOps In The AI Era - A Critical Recalibration

In March 2026, CloudZero’s Ben Austin, Director of Product Marketing, sat down with Ray Rike, Founder and CEO of Benchmarkit, to walk through findings from FinOps in the AI Era: A Critical Recalibration, a joint survey of nearly 500 organizational leaders on how they’re managing or, rather, struggling to manage AI costs.

AI at Superhuman (before it was cool) feat. Loïc Houssier

What does it actually look like to build an AI-native product and lead an engineering team through the AI era when you've been doing it longer than most? Rob Zuber sits down with Loïc Houssier, CTO at Superhuman, to talk about what it meant to be an AI company before AI was everywhere, and how that early foundation shapes the way they build, ship, and think today.

How AI-Powered ATS Systems Are Transforming Modern Recruitment

Recruitment has changed dramatically over the past decade. Companies are no longer relying on manual CV screening and gut-feel interviews. Instead, AI-powered Applicant Tracking Systems (ATS) are reshaping how organizations hire - faster, smarter, and with less bias.

AI-ready sovereignty playbook 2026: how to run gen-AI workloads (ethically) in the EU

Sovereignty is a concept that can have shown nuances in the way it is currently used by states and industry to describe some services. The term “strategic autonomy” has also been used, as to describe the need for governments to ensure that they have a hand on the full value chain (or at least know the gaps and accept the risks) and can apply their rules while it seats in its jurisdiction (autonomy derives from the greek autos (self) nomos (rule).

What Is LLMjacking? The New AI Cybercrime Stealing Cloud AI Compute

LLMjacking is a new cybercrime where attackers steal access to cloud-hosted AI models and use them for free — while the victim pays the bill. In this video, we break down what LLMjacking is, how attackers exploit compromised credentials and exposed APIs, and why security teams should treat AI infrastructure as a high-value attack target. Discovered by the Sysdig Threat Research Team, LLMjacking is quickly becoming the AI-era equivalent of cryptojacking — except instead of mining cryptocurrency, attackers run expensive large language models (LLMs) at scale.