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AI at Work

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How Content Technology is powering Atlassian’s AI quality

Large language models can write fluent answers, but accuracy requires structure. Here’s how we’re building content infrastructure to keep AI grounded.

Article in AI at Work

Giving AI agents design system context from the terminal: what we learned building a CLI

Why ship a CLI when you already have an MCP server? Here’s how giving AI agents design system context from the terminal cut token costs, sped up tasks by 8%, and reached more coding tools.

Article in AI at Work

AI made you faster. It didn’t make you better.

One point four million real workplace conversations with AI were analysed last year. About 5% of people were using it in ways that improved the quality of their work. The “Are you using AI?” conversation is over. Nobody won it. We just stopped having it. But the question we skipped is, “Is anyone reading this […]

Article in AI at Work

Want more confidence in AI? Give it more context.

New Atlassian research finds the bigger predictor of whether AI helps at work isn’t how much people use it — it’s how much of their work it understands.

Article in AI at Work

How one leader rebuilt his feedback loop with an AI agent

Most leaders think they’re good communicators. Fernando Garcia Valenzuela decided to find out.

Article in AI at Work

AI polish makes it harder to spot problems. But there’s a quick fix.

Reviewers were 22% less likely to catch flaws in AI-polished drafts. Atlassian’s Teamwork Lab found a fix that takes five seconds.

Article in AI at Work

When is AI useful, or just a habit? A Play/Pause break helps teams decide.

Knowing when to reach for AI tools at work – and when not to – is an important skill. Teamwork Lab shows you how to refine it.

Article in AI at Work

Building your AI work factory

Most of us do more repetitive work than we realise. Not repetitive in the mechanical sense – not copy-and-paste – but repetitive in structure. This is exactly the kind of problem a work factory is built to solve.

Article in AI at Work

You don’t need a team of AI experts. You just need one.

New research from Atlassian’s Teamwork Lab found that adding a single AI superuser made teams more likely to achieve a top score during ShipIt.

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Introducing Jira as the System of Record for OpenAI Symphony

Scaling AI coding agents requires moving past interactive, single-session CLI workflows. Even for a single developer, running multiple agent loops in parallel gets messy fast. Terminal states get lost, context switching adds noise, and follow-up tasks get dropped. Across a team, that operational friction compounds. OpenAI Symphony handles agent orchestration for Codex. Jira provides the […]

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The tech we love to hate is the tech we’d hate to lose

New research from Atlassian’s Teamwork Lab finds that even as knowledge workers are uneasy about AI’s broader impact, they increasingly embrace it at work.

Article in AI at Work Rovo

Why Rovo is a leading enterprise AI search tool

Rovo Search is now approximately 60% faster! Here are five ways it got more powerful.

Article in AI at Work Rovo

Meet the new Rovo Chat: One prompt, multiple steps, zero hand-holding

The new Long Horizon reasoning engine replaces multi-agent routing with a single reasoning loop — and the results speak for themselves. For the first generation of Rovo Chat, we built what made sense at the time: a multi-agent routing system. One agent for Jira, one for Confluence, one for Slack. Each an expert in its […]

Article in AI at Work

The future of Jira isn’t just tracking work. It’s delegating it.

TL;DR: I wanted to see what would happen if I mapped AI agent capabilities to the functions of a software team. So I built a team of specialized agents in Jira, assigned each one to a stage of the software development lifecycle, and used them to build a new feature for my Forge app. It […]