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JetBrains Launches Air and Junie CLI to Blend Traditional IDE with AI Agents

JetBrains has launched a new “agentic” tooling stack that pairs a multi‑agent development environment, Air, with a standalone, LLM‑agnostic coding agent, Junie CLI. If you know JetBrains , you probably know it for Kotlin , the statically typed Java Virtual Machine (JVM) language used mostly for Android development, or for its well-known integrated development environments (IDEs), such as IntelliJ IDEA for Java, PyCharm for Python, and WebStorm for JavaScript. Going forward, JetBrains hopes you’ll also know it for its AI tools, JetBrains Air and Junie CLI . The first, Air, is pitched as an “agentic development environment” that lets developers delegate coding tasks to multiple AI agents running concurrently. Rather than bolting chat boxes onto editors, Air “builds tools around the agent,” bundling terminals, Git, previews, and code navigation into a single workspace designed to guide and correct agents rather than just prompt them. JetBrains says it’s using its 26 years of IDE ...
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Microsoft Azure Skills Plugin Gives AI Coding Agents a Playbook for Cloud Deployment

AI coding agents are good at writing code. They’re not good at knowing which Azure service fits your workload, which SKU makes sense, what needs to be validated before deployment, or which permissions and quotas matter. That gap between writing code and getting it to production is exactly what Microsoft’s new Azure Skills Plugin is designed to close. Announced March 9 by Chris Harris on the All Things Azure blog, the plugin bundles 19+ curated Azure skills, the Azure MCP Server with over 200 tools across 40+ services, and the Foundry MCP Server for AI model workflows — all in a single install. It works across GitHub Copilot in VS Code, Copilot CLI, Claude Code, and other tools that support the agent plugin and skills patterns. The timing isn’t accidental. This is one of the first major plugins built on the VS Code agent plugin architecture that shipped in VS Code 1.110 just days earlier. And it demonstrates what that architecture looks like when a cloud platform vendor fills it wit...
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Anthropic Code Review Dispatches Agent Teams to Catch the Bugs That Skim Reads Miss

The math was straightforward. Code output per engineer at Anthropic increased by 200% over the past year. Code review didn’t scale with it. Before deploying an automated solution, only 16% of pull requests at Anthropic received substantive review comments. The rest got skim reads. That’s the problem Code Review is designed to solve. Announced March 10 and available now as a research preview for Claude Code Teams and Enterprise customers, Code Review dispatches a team of AI agents on every pull request to find the bugs that quick reads miss. It’s the system Anthropic has been running on nearly every internal PR for months. Now it’s available to customers. How it Works When a PR opens in an enabled repository, Code Review spins up multiple specialized agents that work in parallel. Some probes for data-handling errors, off-by-one conditions, and API misuse. Others perform cross-file consistency checks and reason about intent. A verification step tests each hypothesis to filter false ...
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Report: Lower Performing Software Engineering Teams Benefit More from AI

An analysis of 2,000 organizations worldwide finds that historically lower performing software engineering teams are seeing nearly a 50% improvement in lead time to delivery, a four times greater rate of improvement over higher performing teams that have adopted AI coding tools. Conducted by Plandek, a provider of a software engineering intelligence (SEI) platform, the report also notes that despite that improvement, lower performing software engineering teams still deliver less than half the output per engineer compared to high-performing teams. For example, bottom-quartile teams still take more than 35 hours to merge pull requests, compared to under 21 hours for top performers, according to the report. Top teams also ship software in under 22.5 days on average, while bottom-quartile teams take more than 62 days. High-performing teams complete over two-thirds of their planned sprint work, nearly twice as much as low-performing teams, which complete less than half and regularly mi...
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Five Great DevOps Job Opportunities

DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience. Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers . Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others. The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered. We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration. SimplyHired.com Lenovo Morrisville, NC Senior DevOps Platform Engineer $190,000 to $230,000 LinkedIn Weights & Biases Livingston, NJ Staff DevOps Engineer $188,000 to $275,000 Indeed.com ...
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Why AI-Generated Code Is Raising the Stakes for Secrets Management

Following a $50 million funding round, GitGuardian CEO Eric Fourrier discusses why secrets security is becoming a much bigger problem in the age of AI-generated code and autonomous agents. As more organizations rush to deploy coding assistants and AI agents, Fourrier argues that the number of exposed credentials, API keys and tokens is rising just as quickly, creating new risks for DevSecOps teams already struggling to manage software supply chain security. Fourrier explains that AI agents need access to data and systems to be useful, but many organizations are still handling that access the old way by handing over secrets. That, he says, is accelerating an already serious problem. Secrets are ending up in code, collaboration tools, tickets, developer laptops and other places where they can be exposed, reused or stolen. While early concerns focused on whether large language models themselves might reveal secrets from training data, Fourrier says the bigger issue now is how humans...
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On-Call Rotation Best Practices: Reducing Burnout and Improving Response 

It’s 2:47 a.m. Your phone buzzes. An alert fires again. You acknowledge it, diagnose the issue half asleep, patch it, write a quick note and crawl back to bed. Three hours later, you’re at your desk like nothing happened.   If that sounds familiar, you’re not alone. On-call duty is one of the most important — and most mismanaged responsibilities in engineering. If done right, it protects your systems and distributes the load fairly. If done wrong, it destroys team morale and drives your best engineers to the door.   According to the 2024 State of Engineering Management Report, 65% of engineers reported experiencing burnout in the past year. On-call stress is a major contributing factor, and it compounds quickly when rotations are poorly designed, alert noise is high and there’s no automation to catch the easy stuff.   This guide covers the on-call best practices that high-performing SRE and platform engineering teams actually ...
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