14 posts tagged with "ai"

Confluence holds your team's diagrams in two worlds, and the two don't talk to each other.

Text-defined diagrams — Mermaid and friends — are what AI speaks natively. They're source code, so a chat agent can read them, write them, and revise them. We shipped exactly that last week: an AI agent for Mermaid in Confluence.

Whiteboards are the other world. Freeform shapes, hand-drawn lines, arrows you drag where they need to go — the format that's actually intuitive to put in front of people. Every team has one: the architecture board on the team page, the incident flow everyone references, the onboarding map. And AI couldn't touch any of it. Ask a chat agent for a change to a whiteboard and you'd get a text description of a change, and someone still had to open the canvas and do it by hand.

That gap is why whiteboards go stale. Editing shapes one drag at a time is slow enough that nobody does it, and AI — the thing that was supposed to make maintenance cheap — had no way in.

The AI agent in Excalidraw Plus Whiteboards & AI Agent for Confluence closes the gap: the whiteboard stays what it always was — visual, intuitive, for people — but now you can operate it in plain language, from Confluence chat.

Confluence chat can write your page, summarize the thread, and answer questions about the space. What it couldn't do — until now — is draw. If you asked for "a flowchart of our deployment process," you got a text description of a flowchart, and someone still had to open an editor and build it.

The AI agent in Mermaid Plus Diagrams & AI Agent for Confluence closes that gap: it creates, reads, updates, and converts Mermaid diagrams directly on Confluence pages and blog posts, from chat.

In 2004, John Gruber released Markdown with a modest pitch: a text format that is easy to read, easy to write, and converts cleanly to HTML. Twenty years later his side project became the default language of technical documentation — GitHub renders it natively, Stack Overflow is built on it, Obsidian and Notion popularized it, and every major LLM speaks it fluently. The Verge called it "the default language of the AI era."

That last part is the interesting one. Markdown didn't just survive the AI transition — it locked in because of it. This article walks through why, and what it means for how your team should write documentation inside Confluence.

If you have heard the name "Atlassian Rovo" and are not sure whether it is a new product, a rebrand of Atlassian Intelligence, or something your team should adopt, you are not alone. The naming overlap confuses most teams. This guide explains what Rovo is, how it relates to the AI features you may already be using, and what it changes for the people who administer Atlassian Cloud.

Code reviews happen in pull requests, but the decisions and context often get lost. When someone asks "why did we implement it this way?" the answer is buried in a closed PR with hundreds of comments.

Documenting code reviews in Confluence preserves this context. AI can automate the documentation, turning unstructured PR discussions into organized knowledge. This guide covers how to use AI for code review documentation.

Documentation quality degrades over time. Pages get outdated, formats drift, and gaps emerge as the team and product evolve. Manual review is time-consuming and often inconsistent.

AI can automate documentation review, identifying quality issues and improvement opportunities at scale. This guide covers how to use AI for Confluence documentation review, from audits to automated reports.

Sprint planning is part science, part guesswork. Teams estimate velocity based on gut feeling, leading to overcommitment or underutilization. AI can replace this guesswork with data-driven predictions.

This guide covers how to use AI for Jira sprint planning, from velocity prediction to scope recommendations.

Every new Jira issue needs decisions: What type is it? Who should work on it? How urgent is it? These decisions happen manually today, taking time and introducing inconsistency.

AI can automate these decisions by analyzing issue content, historical patterns, and team context. This guide covers how to use AI for Jira workflow automation, from triage to assignment.

Meetings are where decisions get made, but the notes rarely get written. Someone volunteers to take notes, gets distracted by the conversation, and the team leaves with fragmented memories and no clear action items.

AI meeting notes solve this by automatically capturing, transcribing, and summarizing meetings. This guide covers how to set up AI meeting notes in Confluence, from transcription to follow-up.

Confluence search is powerful but often frustrating. You know the information exists, but your search queries don't find it. Keywords miss synonyms, related content uses different terminology, and results bury the answer under irrelevant pages.

AI-powered search fixes this by understanding intent instead of just matching keywords. This guide covers how to use and optimize AI search in Confluence.

Teams waste hours searching Confluence for answers that already exist. A chatbot can bridge this gap by providing instant answers from your knowledge base, right where the team works.

This guide covers how to build an AI chatbot for Confluence, from no-code solutions to custom development.

Writing good documentation takes time. Between drafting, editing, formatting, and organizing, creating a single Confluence page can take hours. For teams with dozens of pages to maintain, this time adds up quickly.

AI tools like ChatGPT can accelerate every stage of the documentation workflow — from initial drafts to final polish. This guide covers how to use AI in Confluence, from native features to third-party integrations.

Your team has hundreds of Confluence pages, but finding the right information still takes too long. Search returns too many results, keywords miss synonyms, and context gets lost across pages.

RAG (Retrieval-Augmented Generation) changes this by combining search with AI generation. Instead of returning pages, it returns answers — synthesized from your Confluence content. This guide covers how to build a RAG system with Confluence.

The quality of a Jira ticket determines how quickly work gets done. A vague description like "fix the login bug" creates hours of back-and-forth. A detailed description with acceptance criteria, edge cases, and technical context lets developers start immediately.

AI can transform brief summaries into comprehensive tickets. This guide covers how to use AI to write better Jira descriptions, from prompt templates to review workflows.