#atlassian · 8sync News

Atlassian’s guidelines for writing with AI

Atlassian's Brand team shares their internal guidelines for using AI in writing. The framework establishes six core principles: humans lead all writing and ideation, every published piece requires a human byline, AI must be given proper context, all AI-generated facts must be verified, content must meet a human quality bar, and writers must guard against AI erasure of underrepresented perspectives. The guidelines categorize AI use cases into encouraged (gap analysis, stress-testing messaging, structuring notes), proceed-with-caution (social posts, tone rewrites, SEO), and prohibited (generating statistics, inventing product claims, determining core arguments). Atlassian commits to revisiting these guidelines every three months as AI capabilities evolve.

Transforming teamwork: Unlocking AI-driven success in cloud

Atlassian promotes its Teamwork Collection (Jira, Confluence, Loom, Rovo) as the foundation for AI-driven cloud transformation. Based on an executive research report developed with AWS, the piece argues that cloud migration only delivers value when teams consolidate tools and connect planning, knowledge, and communication. Three pillars are outlined: building a connected intelligence layer via the Teamwork Graph, improving async collaboration with Loom (citing 232% ROI from a Forrester study), and embedding AI through Rovo across existing workflows. The core message is that disconnected tools undermine AI adoption, and a unified platform accelerates results.

HR in the age of abundant intelligence – what we believe

Atlassian's Chief People Officer outlines five working hypotheses about how AI will reshape HR over the next 12 months. Key beliefs include: HR must shift from managing headcount to architecting capacity (human + agentic), org design should organize around employee journey outcomes rather than functional silos, data must evolve from records to rich contextual intelligence, talent programs need to move from fixed annual cycles to fluid skills-based models, and human HR practitioners remain essential for relationship-driven judgment that AI cannot replicate. Atlassian is actively testing these hypotheses with new roles like a Director of Capacity Planning for Human & AI, skills-based staffing models, and AI-assisted manager enablement tools.

Trace packages back to their source pipeline

Bitbucket Pipelines now links published package artifacts to their source pipeline run. When a package is published via the native Pipelines integration, a 'Source Pipeline' reference appears in the artifact details view, letting teams quickly trace any artifact back to the exact build that created it. This eliminates the need to manually cross-reference timestamps, commits, and pipeline history, and enables faster debugging by jumping directly from an artifact to its associated build logs.

Bring any data into Atlassian system of work

Teamwork Graph Connectors for Forge are now generally available, enabling developers to ingest external data from SaaS products, internal platforms, or custom systems into Atlassian's Teamwork Graph. Once ingested, data becomes accessible across Rovo Search, Rovo Chat, Rovo Agents, and Atlassian Analytics. The post covers the four-layer architecture (connection definition, ingestion model, execution model, surfacing model), the graph:connector module manifest structure, capabilities declarations (permission replication, sync fidelity, incremental sync), admin configuration form design, and the onConnectionChange lifecycle hook that initializes scheduled ingestion tasks. Part 2 covers production-grade orchestration including scheduling, fan-out, and failure handling.