Operations | Monitoring | ITSM | DevOps | Cloud

People run Codex on their own laptops. When Codex is signed in with a ChatGPT subscription, OpenAI’s own admin console shows who used it and how much: messages and credits. What it doesn’t show is what any of that usage was for, or how it compares to what your team spent on other AI tools. The CloudZero desktop agent for macOS installs on a Mac, sees the traffic from AI coding tools, and prices what those tools use.

Budget season is starting and most finance teams are finding the AI line is the most evasive line on the page. You lived through the year. AI spend came in higher than planned and moved in ways nobody could foresee or forecast. And when the board asked what it produced, the honest answer probably was “we’re working on it.”

TLDR: When you work in Harness AI Chat, your work doesn't stop at Harness. Your pipelines live here, but the change you actually need to make might be a YAML file in GitHub, a Jira ticket, or a Confluence doc. So we built an MCP Gateway inside Harness that lets AI Chat reach those third-party apps for you: safely, under Harness's own access controls and secrets, and without dropped sessions across our distributed fleet. This is the story of what we built and why.

As testing workflows become more AI-assisted, fast access to accurate documentation matters more than ever. Whether you're writing a new load test, troubleshooting an issue, or having an AI agent generate a script for you, you need reliable guidance that keeps pace with the way you work. But most documentation still lives in a browser. Every time you or your agent needs to verify an API or look up a best practice, you're forced to leave your terminal or editor and interrupt your workflow.

‍ Most of the AI features that shipped into observability tools this year summarize alerts. You get a paragraph that restates the dashboard you were already looking at, and the agent never reads the cluster itself, because giving it cluster access is a security conversation nobody wanted to start. This walkthrough starts it.

Builder in the loop is a Mezmo interview series with the engineers, product leaders, and operators shaping AURA. Each installment looks past the product layer to explore the decisions, tradeoffs, and lessons involved in building agents for real production work. This installment features Mike Shearer, the engineer who built AURA and, until recently, its only developer. AI agents are easy to believe in when the task is small.

Server dashboards can look healthy while users wait. Only 51% of the 1,000 most popular mobile sites pass Core Web Vitals, according to the HTTP Archive's 2025 Web Almanac. Closing that gap is the job of digital experience monitoring tools. Some watch customers on your website and mobile apps, others watch staff on laptops and virtual desktops, and a third watches the network in between. Pick the wrong type and you lose a review cycle.

Most IT teams learn their application feels slow when a customer complains. Server metrics never measure what a person on a phone waits for. The best real user monitoring tools close that gap by collecting timings from your users' browsers. Choosing one got harder this year, because the measurement standard moved. In this blog, we compare the best tools for real user monitoring, including their pros, cons, and key features. By the end you will know which one fits your stack.

Data pipelines are systems for moving and processing data. They are made up of concatenated services and data stores that programmatically ingest data from upstream sources; filter, transform, enrich, and route that data; and deliver it to downstream consumers.

Why should I buy software or SaaS if Claude or Codex can build exactly what I want?