Operations | Monitoring | ITSM | DevOps | Cloud

Shipped: AI dimensions, organized around how your team works

AI Signals adds a standard set of AI dimensions to your dimension picker, plus custom dimensions built from your own data. Not every team needs all of them. The ones you don’t use make the ones you want harder to find. Cleaning that up used to require an engineer to run a script every time an account team needed a change. We identified the bottleneck and automated it, so account teams can manage dimensions themselves and engineering stays focused on product work.

Your AI Agent Can Act Across Enterprise Systems. Now You Need Delegation of Authority

In 2025, attackers reached the Salesforce data of hundreds of companies without breaking into Salesforce. They used access tokens belonging to an AI chat agent. Google's threat intelligence team counted more than 700 potentially affected organizations, and because the tokens belonged to a trusted app, they carried the attackers past safeguards such as MFA. Enterprises already know how to manage this kind of risk for people.

Building a local AI agent in Workshop on Ubuntu

Build a fully local AI coding agent on Ubuntu using Canonical Workshop and the Qwen3-Coder Inference Snap. In this step-by-step demo, you'll set up an isolated development environment, connect your coding agent to a locally hosted LLM, and run coding tasks without an internet connection. Follow along to recreate the setup on your own machine, using CPU or GPU inference. Chapters.

Agent Observability FAQ: Understand the value of monitoring, evaluating, and improving your AI agents

Agents have quickly become part of the tech landscape as organizations embrace AI across their businesses. But embracing agents also means embracing the monitoring best practices that have long been part of engineers' workflows: tracking performance, catching errors, and responding to incidents But it doesn't stop there. Agents also introduce new challenges.

What Is Agentic Operations and Why Ops Teams Are Adopting It

AI coding tools have changed how fast software ships. They haven’t changed who runs it. Every extra release, alert and incident still lands on the same SRE, Platform and DevOps teams, and 78% of technology leaders report more incidents since adopting AI-generated code. So operations teams are turning to agents too. Six in 10 enterprise leaders are piloting, deploying or evaluating automated incident response, according to Caylent.

Why AI Coding Agents Are Breaking Your Software

While AI coding agents increase initial build speed, they often lack context about real-world user behavior, leading to downstream production bugs. Watch to learn how to move beyond basic agent-on-agent reviews and set up a true "Quality Factory" using: Check out our demos or get started free: speedscale.com.

How much does it cost to train an AI model? Frontier figures, graded [2026]

Training an AI model costs anywhere from a few hundred dollars to several hundred million, depending entirely on what “train” means. Fine-tuning an existing model: tens to thousands of dollars. Pretraining a small model from scratch: tens of thousands to millions. A frontier run in 2026: hundreds of millions, with Epoch AI projecting the largest runs will pass $1 billion by 2027.