In short: The Atlassian Teamwork Graph is the context layer that connects your people, work, knowledge, conversations, code, assets and data across Atlassian and your other tools. It now holds more than 250 billion connected objects, and it is what Rovo and AI agents use to understand how your business works, within your existing permissions.

Introduction: The Evolution of Teamwork in the Atlassian Ecosystem

The landscape of digital collaboration has undergone a remarkable transformation within Atlassian Cloud. What began as isolated tools for project tracking and documentation has evolved into an interconnected ecosystem where information flows seamlessly across platforms. At the heart of this evolution lies a sophisticated intelligence layer that most users never see but constantly benefit from: the Atlassian Teamwork Graph.

This invisible yet powerful foundation acts as the neural network of modern teamwork, connecting every project, person, and piece of knowledge within your organization. While teams interact with familiar tools from the Atlassian Teamwork Collection, the Teamwork Graph works behind the scenes, transforming disconnected data points into contextual intelligence that makes collaboration smarter, faster, and more intuitive.

Let's explore how the Atlassian Teamwork Graph transforms fragmented information into connected insights that power the future of work.

Why trust this guide: Empyra is an Atlassian Platinum Solution Partner with 80+ certified consultants and 1,000+ customers, including Rakuten, Victoria's Secret, Houston Community College, Sally Beauty and UC Office of the President. Product facts in this guide are checked against Atlassian's announcements and documentation, last checked on 13 November 2026.

What Is Atlassian Teamwork Graph?

1. The Atlassian Teamwork Graph is a unified data and intelligence layer that redefines how teams collaborate.
2. Instead of treating each tool as a separate information silo, it creates a comprehensive map of relationships between people, work, and knowledge across the entire Atlassian ecosystem.
3. If the Atlassian Teamwork Collection is the engine powering team productivity, the Teamwork Graph is the intelligent fuel system ensuring everything runs smoothly and efficiently.
4. It's not a separate product or manual feature, it's a core capability embedded within Atlassian Cloud, continuously working in the background to enhance every interaction.
5. Serving as the foundation of the Atlassian AI platform, it now maps more than 250 billion connected objects to understand how your organization operates (Atlassian).

It recognizes that a Jira ticket isn’t just a task—it’s:

• Connected to team members
• Linked to strategic goals documented in Confluence
• Discussed in Loom videos
• Related to numerous other work items across your digital workspace

7. Within the Atlassian platform architecture, the Teamwork Graph acts as the connective tissue, unifying disparate data sources into a single intelligence layer.
8. This enables smart, context-aware experiences such as:
   1. Surfacing the right document before you even search for it
   2. Identifying who has the expertise to unblock your project
   3. Providing relevant context at the exact moment you need it

How Atlassian Teamwork Graph Works

The power of the Atlassian Teamwork Graph lies in its ability to map and understand three fundamental dimensions of organizational work:

People Connections:

The Graph tracks how individuals work together, understanding team structures, reporting relationships, collaboration patterns, and areas of expertise. It learns who regularly collaborates on specific project types, which subject matter experts to consult for particular challenges, and how information flows through your organization.

Work Relationships:

Every project, issue, epic, and task exists within a web of dependencies and associations. The Atlassian Teamwork Graph connects these elements intelligently, understanding that a bug fix might relate to a feature request, which connects to a product roadmap, which links to strategic objectives documented across multiple Confluence spaces.

Knowledge Networks:

Documentation, decisions, discussions, and institutional knowledge scattered across tools become interconnected nodes in the Graph. It understands semantic relationships between content, recognizing when a design document relates to implementation notes, when meeting recordings contain decisions affecting current work, and when historical context might inform present challenges.

This mapping happens across the entire Atlassian data intelligence ecosystem - Jira Software and Work Management, Confluence, Loom, Atlassian Rovo AI, and even third-party applications integrated through the platform. Machine learning models continuously analyze these connections, identifying patterns that help predict what information teams need, when they need it, and how different pieces of work relate to each other. Early Atlassian benchmarks show teams using connected products through the Teamwork Graph experience up to a 25% reduction in time spent searching for information and 30% faster task resolution with AI-powered recommendations

Seven Context Types Since Team '26 Europe

At Team '26 Europe in October 2026, Atlassian expanded these three dimensions into seven context types (Atlassian):

How the Teamwork Graph Shows Context in Jira

When you open a Jira work item, Confluence page or Loom video, the Teamwork Graph surfaces three kinds of signals (Atlassian):

• Related work: connected issues, pages and runbooks across apps, so you spend less time searching.
• People signals: the owners and recent contributors, so you know who can help.
• Decision trails: upstream and downstream dependencies during a change, so nothing falls through the cracks.

How accurate these signals are depends on your data. Clear links between work items, pages and goals, plus clean permissions, give the graph better signals to work with. Missing links or over-restricted spaces give it less.

This mapping happens across the entire Atlassian data intelligence ecosystem, Jira, Confluence, Loom, Atlassian Rovo AI, and even third-party applications integrated through the platform. Machine learning models continuously analyze these connections, identifying patterns that help predict what information teams need, when they need it, and how different pieces of work relate to each other. In Atlassian's internal benchmarks, agents grounded in Teamwork Graph data delivered 44% more accurate results while using 48% fewer tokens (Atlassian).

Key Features and Capabilities

Together, these capabilities make the Atlassian Teamwork Graph the connective tissue of the Atlassian ecosystem.

Real-World Benefits for Teams

In addition to its technical sophistication, the Atlassian Teamwork Graph delivers measurable business impact. Here are seven ways it helps organizations accelerate teamwork and achieve better outcomes:

1. Breaks Down Silos Across Teams and Tools

Connects Jira, Confluence, Slack, and third-party apps to eliminate duplication and miscommunication.Outcome: Seamless collaboration and faster cross-department handoffs.

2. Powers AI-Driven Knowledge Discovery

Surfaces relevant content instantly through Atlassian Rovo AI, saving hours spent searching for information.Example: A new hire can find onboarding resources from multiple tools in seconds.

3. Delivers Context-Aware Automation with Rovo

Uses contextual understanding to suggest next steps, route issues, and summarize updates automatically.

4. Enables Personalized Workflows and Insights

Provides tailored dashboards and recommendations based on roles and recent activity.

5. Boosts Self-Service and Employee Empowerment

Creates intelligent self-service options that improve SLA performance and reduce ticket volume.

6. Connects Strategy to Execution

Maps every Jira issue or epic to organizational OKRs, helping leaders monitor progress and alignment.

7. Unlocks Continuous Improvement Through Insights

Identifies trends and bottlenecks, turning the Atlassian ecosystem into a living system of improvement.

Mercedes-Benz is one example. After building custom Forge connectors for its defect, requirements and release systems, Atlassian reports a 90% improvement in defect intake quality, 85% faster duplicate detection and 10x faster software delivery (Atlassian).

Atlassian Teamwork Graph vs. Atlassian Teamwork Collection

Understanding the relationship between Teamwork Graph vs Teamwork Collection clarifies how these concepts work together to enable modern collaboration:

The Atlassian Teamwork Collection represents the suite of products and tools that teams directly interact with daily, Jira, Confluence, Loom, Rovo and related applications. These are the visible interfaces where work happens: where issues are tracked, documentation is created, videos are recorded, and projects are managed. Teams purchase Atlassian licenses for the Collection and use these tools as their primary collaboration environment.

The Atlassian Teamwork Graph, by contrast, is the intelligent data layer operating beneath these tools. It's not something you buy separately or access directly; rather, it's the foundation that makes the Collection smarter. Think of the Collection as the instruments in an orchestra, each valuable individually, while the Graph acts as the conductor, ensuring they work together harmoniously.

This distinction matters because the value you derive from the Atlassian Teamwork Collection multiplies exponentially thanks to the Teamwork Graph. A Confluence page isn't just a static document; it's connected to related Jira work, authored by team members with specific expertise, and linked to strategic objectives. A Jira issue isn't isolated; it's understood within the context of project history, team capacity, and organizational priorities.

Organizations adopting more tools from the Collection automatically benefit from richer connections within the Atlassian Teamwork Graph. The more surfaces of your work that exist within the ecosystem, the more powerful the Graph becomes at surfacing relevant context and enabling intelligent automation.

The Role of Teamwork Graph in Atlassian's AI Vision

The Atlassian Teamwork Graph forms the foundational infrastructure for Atlassian’s vision of AI-augmented teamwork.

Unlike many AI tools that operate in isolation with limited context, Atlassian Rovo AI agents leverage the Graph’s comprehensive relationship map to deliver truly intelligent, context-aware assistance.

This deep integration enables Rovo agents to act as knowledgeable team members, not just chatbots.

When you ask Rovo about a project’s status, it:

• Understands project relationships and dependencies
• Recognizes team roles and responsibilities
• Considers recent activity and historical patterns captured within the Teamwork Graph
• Provides nuanced, organization-specific responses that reflect real work dynamics

Future capabilities powered by the Atlassian Teamwork Graph will unlock cross-tool analytics, revealing insights that single tools can’t show, such as:

• How different teams approach similar challenges
• Which collaboration patterns drive successful outcomes
• Where process improvements can lead to significant productivity gains

Predictive insights will mark the next major evolution:

• AI models will analyze historical project patterns, issue escalations, and response behaviors
• The system will forecast potential problems before they occur
• It will suggest optimal resource allocation and recommend proactive interventions

The Atlassian Teamwork Graph ensures all AI-driven insights remain grounded in your organization’s unique context, not abstract algorithms.

Its intelligence reflects:

• How your teams actually work
• What knowledge truly matters
• Which connections deliver the most value within your environment

Two newer examples show where this is heading. Rovo Work runs long, multi-step AI tasks on the same graph context, and Atlassian AI agents and AMP give agents their own identity while they draw on the graph.

What's New in the Atlassian Teamwork Graph After Team '26 Europe

At Team '26 Europe in Amsterdam in October 2026, Atlassian announced updates that widen what the Teamwork Graph can see and who can use it. The graph has grown to more than 250 billion connected objects, up from 150 billion in May 2026.

Sources: Atlassian, platform announcement, Atlassian, Insights, Atlassian, Code Context and Atlassian, Teamwork Graph. For every announcement, see our Team '26 Europe overview.

Rovo Connectors

Rovo connectors bring content from tools outside Atlassian into the Teamwork Graph, so Rovo Search, Chat and agents can use it. There are three types (Atlassian Support):

• Synced: stores and indexes all content in a workspace, for the best search quality. Counts toward your indexed objects allowance.
• Direct: retrieves content live from the tool, with nothing stored (for example Gmail, Outlook Mail and Slack).
• Smart Link: indexes only links a user has seen in Atlassian. Always on.

If no ready-made connector exists, our Atlassian integration services team can build a custom one on Forge.

Teamwork Graph CLI and MCP

The Teamwork Graph CLI gives coding agents such as Claude Code, Codex and Cursor access to your Atlassian data and the graph from the terminal. It has more than 300 commands and is now generally available (Atlassian Support). For agents in a browser, on mobile or in the cloud, Atlassian recommends the Atlassian Rovo MCP server instead (Atlassian Support).

Some Teamwork Graph calls use Rovo credits. Writes and single-product lookups are free, while enriched calls that combine several sources mostly cost 1 to 10 credits each. See our guide to Rovo credits per user to plan for it.

How Organizations Can Leverage Teamwork Graph Today

While the Atlassian Teamwork Graph operates automatically for all Atlassian Cloud customers, organizations can maximize its value through strategic adoption approaches:

Atlassian Cloud Products:

Represents the foundational step. Since the Graph's power grows with the breadth of data it can connect, migrating workloads from on-premises deployments to Cloud and consolidating collaboration within the Atlassian ecosystem amplifies the intelligence available to your teams. Each additional tool adopted adds new dimensions to the relationship map, unlocking richer insights.

Adopt the Complete Teamwork Collection:

To experience the full potential of connected teamwork at Atlassian. Organizations using only Jira miss the connections between work and documentation. Those lacking Loom lose the context embedded in asynchronous video communication. Rovo AI delivers maximum value when it can draw upon the comprehensive knowledge graph built from all collaboration surfaces working together.

For Administrators and Platform Leaders:

Several practical steps enhance how the Atlassian Teamwork Graph serves your organization:

Integrate relevant third-party data sources through Rovo connectors and Atlassian's robust API ecosystem, extending the Graph beyond native tools to include external systems where teams work. Enable AI features across products, ensuring teams can benefit from Rovo AI agents and intelligent recommendations. Review and optimize access controls to balance information discoverability with appropriate security boundaries, the Graph respects permissions, but overly restrictive settings can limit valuable cross-team connections.

Encourage consistent metadata practices across teams. While the Teamwork Graph understands relationships automatically, structured information like properly labelled components, accurate sprint dates, and well-organized Confluence spaces helps the Atlassian data intelligence system provide even more precise insights.

Monitor adoption patterns to identify where teams might benefit from additional Atlassian training or where workflow improvements could enhance collaboration. The same connections powering user-facing features can help administrators understand how effectively the organization leverages the platform.

Conclusion: The Future of Connected Work

The Atlassian Teamwork Graph is more than just a backend feature, it's the foundation of intelligent, connected teamwork. As work grows more complex and distributed, understanding relationships between people, projects, and knowledge becomes essential. By connecting data across the Atlassian AI platform, the Teamwork Graph enables faster decisions, improved collaboration, and AI-powered productivity. It eliminates friction, reduces context switching, and helps teams work smarter, not harder.

The future of work depends on connected intelligence, not more tools, and the Atlassian Teamwork Graph makes that future a reality today.

Explore how Empyra can help your organization harness the full potential of the Atlassian ecosystem. Our experts optimize Atlassian implementations to help teams leverage the Teamwork Graph for measurable impact. Contact us today to unlock smarter collaboration and data-driven teamwork.