Operations | Monitoring | ITSM | DevOps | Cloud

Four ways engineering teams use the Datadog MCP Server to power AI agents

Since the Datadog Model Context Protocol (MCP) Server first launched in Preview, Datadog has experienced an overwhelming amount of interest and feedback from customers. We appreciate those who requested access to test our product, provided feedback, and shared their stories of how the MCP Server helped them overcome engineering challenges.

Seedance 2.0 vs Traditional Production: Is AI Finally Production-Ready?

Every few years, a new tool appears that forces the creative industry to pause and reassess its assumptions. In 2026, that conversation is happening again, this time around AI video. The question is no longer whether AI can generate impressive demo clips. That phase is over. The real question is far more consequential.

AI for Operations Teams: Using Legal Awareness to Reduce Risk and Improve Decision-Making

Operations teams sit at the center of most organizations. They coordinate processes, manage vendors, support compliance requirements, and ensure that day-to-day activities run smoothly. While their role is often associated with efficiency and logistics, operations professionals increasingly find themselves interacting with another critical area: legal documentation.

AI Systems Status Report - February 2026

This report covers the operational status of major AI systems during February 2026, including Anthropic, Cohere, DeepSeek, Google Gemini, Groq Cloud, OpenAI, Perplexity, Replicate, and xAI. The data includes official incidents reported on vendor status pages and unconfirmed incidents detected through IsDown's monitoring systems.

Avoiding Common Mistakes When Using AI Content Tools

AI writing tools are everywhere. They're fast, affordable, and impressively capable. But somewhere between "generate" and "publish," things go sideways for a lot of people. The problem isn't the technology itself. It's how people use it. Hand someone a power drill, and they can build a deck - or put a hole through a water pipe. Same tool, wildly different outcomes. Most mistakes with AI writing tools are preventable. This article breaks down the biggest ones and shows you how to sidestep them before they cost you traffic, credibility, or both.

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.

Why the AI market is shifting

The AI revolution is getting expensive. Ben Norris (AI Engineer at Civo) breaks down a staggering statistic: AI token usage has jumped from 9.8 trillion to 1.3 quadrillion in just under two years—a 130x increase. As businesses scale, the "closed source" premium is becoming a bottleneck. Watch as Ben explains why enterprises are turning toward democratized, open-source AI and smaller vendors like relaxAI to maintain power at a fraction of the cost.

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.