The hidden revolution: How Atlassian's AI becomes truly usable through Forge & Teamwork Graph

While Rovo was widely discussed in Barcelona, the real magic is happening one layer deeper: Forge and the Teamwork Graph are becoming the platform that makes AI features not just “nice to have,” but business-critical. This article deliberately does not focus on the already summarized Rovo headlines found elsewhere, but instead shows how teams can now use Forge to quickly unlock solid value—from data integration and governance to reusable agent capabilities.

Atlassian Intelligence: AI in everyday life, not as an add-on

Rovo doesn’t stand on its own. Atlassian Intelligence runs through Jira, Confluence, and Loom, and shows its value in practical everyday scenarios:

• Jira Task Breakdowns automatically break complex initiatives into manageable work packages.
• Audio summaries in Confluence make content easy to consume on the go.
• Video-to-Issue in Loom turns screen recordings directly into structured Jira tickets — including acceptance criteria.

These aren’t gimmicks but features that make a noticeable difference in everyday work. When a 10-minute Loom recording is automatically turned into a complete issue, it changes the workflow.

Atlassian links knowledge, work, and people in a shared data model: the Teamwork Graph. For developers, this means apps can read and populate this model — forming the basis for context-rich experiences, search results, and AI automations that go beyond Jira, Confluence & Co.

Forge: The foundation for extensions

While Rovo gets most of the attention, Forge works behind the scenes in the engine room. The platform provides access to the Teamwork Graph — the connected data model of issues, people, projects, and their relationships. This enables capabilities that act context-aware across products.

What this means for organizations

• Custom Intelligence: Developers create their own Rovo Skills that access proprietary systems. This allows Rovo to answer questions about your CRM, access legacy data, or orchestrate workflows across multiple platforms.
• AI-Assisted Development: Rovo Studio and Forge enable a new development mode: describe requirements in natural language, generate the scaffolding, then harden and extend it technically.
• Cross-Product Automation: Forge powers workflows across Jira, Confluence & co. — with reasoning over the Teamwork Graph for smarter, context-aware operations.

Rovo Studio + Forge: Low-code meets “production-grade”

Rovo Studio brings together the key building blocks for creating solutions (agents, automations, assets, hubs, apps). This allows power users to quickly build working prototypes — and development teams can make them production-ready with Forge (including policies, secure secrets, monitoring/observability, audits).

Architectural patterns with rapid effect

1. Bring in your own context (Teamwork Graph)

Connects to external systems such as CRM, PLM, data warehouse, or DMS. This makes their data available in search, chat, and agents—answers are based on your company's reality, not just Atlassian data.

2. Skill library instead of uncontrolled growth

Start with a few tested skills and clear permissions (e.g., issue creation with acceptance criteria, asset lookup, approvals). Version, monitor, roll out — and curate centrally.

3. from studio prototype to forge curing

Transfers successful flows to Forge and adds secrets management, rate limits/retry, audit trails, feature flags and rollbacks. This makes prototypes robust and audit-proof.

4. smart links as UI glue

Uses context-rich link previews to save clicks and increase adoption - especially in Confluence collections and project overviews.

Assets as a platform component

An important signal: Assets is no longer “just” a CMDB in the JSM context but is intended as a platform-wide building block. This makes object data (configurations, devices, contracts, master data) searchable, automatable, and AI-ready — usable across products via Forge and Rovo. For XALT customers with complex IT landscapes, CIs, or relationship models, this opens up new possibilities for transparency and control.

ConclusionConsider „assets“ as an object layer for AI flows (e.g. „Find available laptops with M-chip, location X, warranty < 3 months“). Agents can act on this basis, not just respond.

Operational maturity: security, rights, visibility

In order to move from prototype to production, these guard rails should be established to ensure security, access control and operational transparency.

• Governance & authorizations: Defines who can create agents and skills, which access rights (scopes) are required and which actions require confirmation.
• Visibility & activation: Ensures that Rovo and Studio are visible and activated for the right user groups - including checking plans/editions.
• Monitoring & Compliance: Set up logging, metrics, audit events and fallback/rollback early on so that you can track processes and roll them back quickly if necessary.

Convergence: lower hurdles, faster innovation

The decisive factor is not a single function, but the Growing together:

• Non-developers build automations in natural language.
• Developers deliver deep integrations faster — on a shared AI context.
• Teams expand capabilities without waiting for vendor roadmaps.

This democratization is crucial because every organization works differently. Winners adapt tools to their reality — not the other way around.

What this means for your organization

If you are a XALT customer or are considering our solutions, these developments make the following possible:

• Immediate results: The general availability of Rovo means that AI features are accessible today, not in some distant future. We support you in activating these features and integrating them into your workflows.
• Custom solutions: With Forge and Rovo Studio, we can develop tailored AI agents and automations that address your specific challenges — whether it’s automating service requests, extracting insights from documents, or connecting distributed systems.
• Strategic advantage: Organizations that introduce AI-supported workflows early gain cumulative benefits. Early adopters actively help shape how these tools evolve, while competitors watch from the sidelines.

The XALT advantage

At XALT, we not only implement Atlassian products, but also design intelligent Systems of Work. As a partner for AI & System of Work, we support you in this,

• evaluating which AI capabilities deliver immediate ROI in your context,
• designing workflows that use Rovo, Intelligence and Forge effectively,
• building customized solutions with our deep Forge development expertise, and
• enabling your teams to get the maximum value from these AI skills.

The convergence of Rovo, Atlassian Intelligence, and Forge is more than a small update. It marks a fundamental shift in how work gets done — from manual to intelligent, from isolated to connected, from reactive to proactive.

Ready to find out how Rovo, Atlassian Intelligence, and Forge can transform your organization? Contact XALT to discuss your AI-augmented future. Our team of Atlassian consultants and Forge developers will help you move from possibility to reality.