Your team already works in Claude, ChatGPT, and Gemini. The friction comes when those tools have no connection to the actual work: the Jira tickets, the Confluence docs, the decisions your team made last Tuesday. Without that context, AI assistants give generic answers, and your people end up doing the manual work of bridging the gap themselves.

In this session, you will see live demonstrations across Claude, OpenAI, and Gemini, connected to the Atlassian Teamwork Graph in real time. Using the Model Context Protocol, your existing AI tools can search and read your Jira and Confluence knowledge base without duplicating data or requiring anyone to switch windows. You will also see how Rovo connects to external agents like Gemini through Agent-to-Agent communication, so AI tools can trigger real actions across your Atlassian work and hand results back and forth. Every connection runs from wherever your team already operates, including desktop apps, IDEs, and developer interfaces.

This session is designed for engineering leads, IT admins, and team champions who want to extend AI tools they already trust into a connected, governed Atlassian workflow. You will leave with a clear picture of which connection method fits which use case and how to make the case for your team.

Key Takeaways

• Discover how the Model Context Protocol lets Claude, ChatGPT, and Gemini search and read your Atlassian knowledge base in real time, with no data duplication and no manual copy-paste required.
• Learn how Rovo's Agent-to-Agent communication enables external AI tools to trigger updates across Jira and Confluence, so your AI workflows produce actual team outcomes rather than just answers.
• Build a practical framework for choosing between MCP, Agent-to-Agent, and direct integrations based on your team's tools, workflows, and governance requirements.