Teams | Collaboration | Customer Service | Project Management

Teams of all stripes have run billions of cross‑functional, multi‑tool workflows on Atlassian. After decades spent helping them plan, build, ship, and do, those same workflows are now lighting up with millions of agentic automations, up 7x in the last six months alone. All signals point to the rise of the AI‑native organization, where humans operate at critical junctures, deciding what matters and why, and agents do more of the execution.

We’ve all been there – toggling between six tabs, copying content from one tool into another, and wondering if anyone actually read the brief. The promise of AI was supposed to fix this. Instead, most teams got a chatbot bolted onto the side of their screen. We think AI should work the way a great teammate does: show up where the work happens, understand what’s going on, and actually move things forward. Not from a separate window. Not after a five-paragraph prompt.

AI is changing how engineering teams work faster than most organizations can adapt. Coding assistants are now part of the daily workflow, agents are starting to own tasks end-to-end, and the way we deliver software is being redefined in real time. With that shift, engineering leaders are facing a new set of questions. Are these tools actually improving outcomes? Where are they falling short? Which teams are seeing value, and which aren’t?

AI agents are only as good as what they know. Right now, most don’t know enough. Not because the AI is broken, but because the data is. Information is scattered across tools, siloed by department, stripped of the human context that makes it useful. Agents guess. They hallucinate. Teams splinter around different versions of the truth. Context isn’t a file or a ticket. It’s the space in between: why a decision was made, who owns it now, what broke last time.

Freshworks Chief People Officer Johanna Jackman on bringing HR onto Freshservice to streamline operations, improve employee experience, and unlock higher-impact work.

Over the past few decades in the technology industry, some of the biggest constraints to building products have been about having enough engineers, time, or compute. For the first time, that era is ending. Tech teams are experiencing a revolution unlike anything they’ve seen before. The barriers to entry for building have all but disappeared. The constraint no longer comes from producing enough output, but from deciding what to build, and the restraint required to build something good.

ClickUp is one of the most popular project management tools in the world. It’s flexible, packed with features, and built to replace multiple apps like Trello, Asana, Notion, and even Google Docs. But here’s what many teams realise after using it for a few weeks: ClickUp pricing looks affordable at first, but it becomes expensive once you factor in AI add-ons, usage limits, and scaling costs.

Imagine a typical morning for your support lead. A customer, let’s call him John, forgot how to update his billing information. John goes to your website, finds your support email, and sends a message. That email enters your help desk as a ticket. Your agent, Sarah, sees the notification, opens the ticket, identifies the issue, and manually types out the same three-step process she has explained twelve times already today. She hits send, and John finally gets his answer.

A few weeks ago, we soft-launched the Wire Integrations SDK in Staging and called it Phase 1 - the foundation. Today, we're taking the next step. Phase 2 brings the SDK to production in Wire, and with it, Apps become a properly typed, properly visible part of Wire. With this release, Wire becomes a place where automation lives natively: secure by design, end-to-end encrypted, and built on the protocols our customers trust with their most sensitive conversations. Here's what shipping.

A growth marketing team builds an intake rule in Asana that handles their repetitive tasks. The rule handles every incoming request: One branch for high priority requests spins up and assigns subtasks so that the team can get straight to work. Another for medium-priority tasks uses AI to summarize the request and suggest next steps. A third routes low-priority requests to the team's backlog with a due date. The rule is complex, and it works beautifully. Then the sales team wants to use it too.