Atlassian Unveils Agentic Multiplayer Protocol to Enable Humans and AI Agents Working Together
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Atlassian launches AMP to tackle AI code visibility, attribution
Analysts and consultants agree that increased visibility is good, but question whether the offering is good enough to be useful. endif; ?> Atlassian today rolled out new offerings designed to make it easier for IT to differentiate coding by AIs from human coding. The problem is that such
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'Working with AI isn't single player any more -- it's multiplayer': Atlassian CEO Mike Cannon-Brookes lays out what your business needs in the agentic AI age, and why agents won't replace your human workers anytime soon
Atlassian CEO wants its platform can be your company's central nervous system Atlassian has laid out its aim of being the ideal platform for your business to make the most out of its data in the new agentic enterprise era. "We are living through the most creative, dynamic eras in the history of
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Atlassian deepens its OpenAI partnership with a spend commitment -- but its platform stays firmly multi-model
Enterprise project and dev software staple Atlassian said Monday it is expanding a partnership with OpenAI that dates back to 2023, putting OpenAI's newest frontier models -- including the recently released GPT-6 Astra and the GPT-5.6 series -- into Atlassian's platform and its Rovo AI, and
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Team '26 Europe - Atlassian's AI strategy aims to get humans and agents working together
When Atlassian unveiled its Agentic Multiplayer Protocol (AMP) at Team 26 Europe in Amsterdam last week, a lot of attention focused on the right approach to human AI collaboration. On how getting the right mix of tools, governance and management applies as equally to AI agents as it does to humans.
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Atlassian lays groundwork for humans and AI agents to work side by side
Atlassian lays groundwork for humans and AI agents to work side by side Atlassian Corp. today announced Agentic Multiplayer Protocol, a platform update designed to change how humans and artificial intelligence agents collaborate and get work done using shared context and tasks within the same
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Team '26 Europe - Atlassian's Agentic Multiplayer Protocol sets the scene as agents join the team
Are AI agents just another technology tool or are they a wholesale replacement for human beings? Perhaps that's the wrong question, and the true answer lies somewhere between these two extremes. For the opening of its Team '26 Europe conference today, Atlassian is unveiling a swathe of new
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Atlassian Announces New AI Collaboration Innovations at Team '26 Europe
AMP is a collection of experiences, patterns, and technology that enable agents to collaborate alongside human teammates, on and off the Atlassian platform. At Atlassian's conference Team '26 Europe in Amsterdam, the provider of AI collaboration and productivity software for teams has announced
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Atlassian Unveils The Agentic Multiplayer Protocol for Enhanced AI Collaboration at Team '26 Europe
Experiences, patterns, and technology that enable agents to collaborate alongside human teammates // expanded OpenAI partnership // New MCP version uses up to an average of 25% less tokens At Atlassian's conference Team '26 Europe in Amsterdam, the provider of AI collaboration and productivity
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Atlassian Corporation Introduces AMP: The Agentic Multiplayer Protocol To Power Human-AI Collaboration
Atlassian Corporation announced AMP: The Agentic Multiplayer Protocol at its Team '26 Europe conference. Other announcements include a new Atlassian MCP Server, enhancements to the Teamwork Graph, and capabilities that allow customers to collaborate with agents in richer, more visible ways. AMP
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Atlassian launched its Agentic Multiplayer Protocol at Team '26 Europe, introducing a governed framework where humans and AI agents collaborate side by side. The new AMP system includes enhanced AI code visibility, Rovo Work for complex tasks, and EU AI Inference for regional compliance, addressing enterprise concerns about AI governance and attribution.
Atlassian AI Introduces Framework for Governed Collaboration
Atlassian rolled out its Agentic Multiplayer Protocol (AMP) at Team '26 Europe in Amsterdam, establishing what the company describes as its foundation for human-AI collaboration across the Atlassian platform
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. CEO Mike Cannon-Brookes emphasized that working with AI has evolved into a multiplayer system for humans and AI, stating "you don't replace people with agents, you create a new and powerful system of collaboration for your organization"2
. The protocol defines how agents participate in work with assigned identity, scoped authority, shared context, and reviewable results1
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Source: TechRadar
Jamil Valliani, Atlassian's Head of AI Products, explained that lack of meaningful visibility into code origin represents a massive problem holding enterprises back from further enterprise AI adoption
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. AMP addresses this through comprehensive governance, enhanced context via Teamwork Graph, and new interaction patterns between humans and autonomous agents4
.Enhanced AI Code Visibility Through Teamwork Graph
The updated Teamwork Graph now indexes source code down to functions, symbols and classes, enabling developers to search across Bitbucket and GitHub without cloning repositories
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. This advancement in AI-assisted coding provides full attribution and accountability, including version history showing exactly what humans wrote versus what agents produced across Claude, Codex, Figma, and Rovo1
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Source: InfoWorld
IDC Research Manager Adam Resnick noted the potential value is substantial as agents take on more autonomous work, but stressed that enterprises need visibility beyond simple line attribution. Organizations want to understand where humans and agents exercised judgment on consequential decisions, such as which data store to adopt or how to handle authentication
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. Mike Wilkes, Enterprise CISO at Aikido Security, explained that Git can identify who committed changes, but increasingly that differs from who or what actually wrote the code, as developers accept Copilot suggestions, paste code from ChatGPT, or delegate tasks to autonomous agents1
.Rovo Work Handles Complex Multi-Step Tasks
Atlassian introduced Rovo Work, a new mode in Rovo Chat designed for complex, multi-step tasks with human oversight to review and approve actions
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. The system executes across Jira, Confluence, and connected tools, running for hours in a secure sandbox to achieve user-approved goals1
. Valliani described how users give Rovo permission to "unleash itself fully" when sending queries to Rovo Work5
.Rovo can go beyond straightforward task completion by training itself on unfamiliar requests. In one demonstration, a product manager requested an Instagram-ready reel, and Rovo researched the proper instructions, learned the required output format, and obtained video synthesis tooling
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. This represents a material shift from standard AI models asking "is this in my training?" to "how do I learn to do this?"5
.Loom Transforms Visual Instructions Into Agent Prompts
Atlassian demonstrated how Loom enables users to direct agents through simple natural language commands combined with visual references
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. The system captures users pointing at screen elements while speaking instructions like "make this green" or "make this higher," then formats those visual and verbal cues into prompts5
. Valliani explained this mirrors how humans naturally work together, allowing people to show rather than tell what they need4
.Expanded OpenAI Partnership Maintains Multi-Model Approach
Atlassian deepened its OpenAI partnership with a spend commitment, integrating GPT-6 Astra and GPT-5.6 series models into its platform while maintaining its multi-model approach
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. The company explicitly stated GPT-6 Astra is not becoming Rovo's default model, as Rovo runs an internal AI gateway routing dynamically across OpenAI and other providers, balancing capability, speed and cost per task3
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Source: VentureBeat
Atlassian's rebuilt Model Context Protocol server now handles more than 15 million calls daily, exposing 220-plus tools across Jira, Confluence, Bitbucket, Loom and other platforms
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. Internal testing showed up to 25% fewer tokens on comparable Jira and Confluence work compared to the previous version3
.Related Stories
Comprehensive AI Governance and Regional Compliance
Atlassian's AI strategy addresses governance through non-human identity (NHI) controls examining what AI can see and restricting access granted to agent accounts
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. The MCP server enforces OAuth 2.1 and PKCE by default, respecting each user's existing permissions so agents cannot access data beyond user privileges3
. The system separates read, write and destructive actions into distinct risk tiers, requiring explicit confirmation on higher-risk steps3
.EU AI Inference restricts LLM processing exclusively to models hosted within the EU, addressing regional compliance requirements
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. Valliani explained that European customers increasingly desire all workflow elements to remain in their custody, driven by evolving regulations and geopolitical challenges4
. The company negotiated with hyperscalers and major providers to secure capacity for this fundamental infrastructure investment4
.Forward Deployed Engineering Program Accelerates Customer Adoption
Atlassian launched its Forward Deployed Engineering program to help customers at different stages of AI implementation
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. The company's AI engineers work on-site with customers to address real business problems, such as HR departments responding faster to employee requests or support teams handling high ticket volumes with agents4
. Valliani noted customers range from organizations just beginning AI adoption to those managing many agents and seeking meaningful ROI metrics beyond raw pull request counts4
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