Operations | Monitoring | ITSM | DevOps | Cloud

The data context gap: an evaluation guide for agent-ready infrastructure

Why do AI agents that look brilliant in a sandbox fail the moment they hit production? For platform leaders, the answer is a lack of environmental parity: the ability to interact with the exact data state and service topology where the actual bugs live. When an agent attempts to modify a schema, optimize a query, or reproduce a bug without access to the real-world data state, it hits the Data Context Gap.

How AI-Powered Wellness Platforms Are Reshaping HR and Employee Well-Being

As hybrid work continues to redefine how organizations operate, companies are increasingly turning to artificial intelligence to support not only productivity but also employee well-being. Businesses are realizing that technology can play a major role in protecting the mental and physical health of their teams while also strengthening overall organizational performance.

Create a Custom Service Health Board With the Honeycomb MCP

Your software is sending data to Honeycomb. Now where is the dashboard you want? The best dashboard is one created just for your application, or your service, or your team. You can get that in minutes with the Honeycomb MCP. Open your coding agent in your IDE, or on the command line in your code repository. Configure the Honeycomb MCP and authenticate with Read and Write permissions. Now tell it what you want. You can be high-level: Make me a service health board for the frontend service.

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.

You Bought the AI Licenses. Why Is Only One Developer Getting 10x Results?

Here's something nobody talks about at the AI strategy meetings. Your organization just spent six figures on Cursor licenses, Claude seats, and Copilot subscriptions. Ninety percent of your engineers have access. By most internal measures, the rollout was a success. But somewhere on your team, one developer is running circles around everyone else.

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.

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.