Enterprise AI has a context problem. Leading AI models from OpenAI, Anthropic, and Google can draft code, summarize contracts, and plan projects, yet on their own they don’t know why a company killed a product line in 2022, which team owns the billing service, or what last quarter’s customer escalation taught the support organization.

An agent without that knowledge can produce work that looks right but misses the point. As agents shift from answering questions to taking action, that gap can pose an operational risk.

The pressure is mounting. Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by the end of 2026, up from less than 5% in 2025. Organizations deploying agents from multiple vendors must reconcile separate session histories and, in some cases, actions taken with borrowed human credentials.

That is the backdrop for Atlassian ‘s Team ‘26 Europe conference in Amsterdam this week, where the company introduced its Agentic Multiplayer Protocol . AMP is Atlassian’s bid to provide the institutional context that tells agents how a specific business works.

The stakes extend well beyond Atlassian. Platforms that provide and govern access to that context could exert substantial influence over where agents operate and which vendors capture economic value.

Atlassian describes AMP as a framework for bringing AI agents into everyday teamwork alongside people. It combines access to organizational context, visibility into agent activity, and a growing set of governance controls.

Although Atlassian calls it a protocol, the announcement describes integrated platform capabilities; the company has not published a specification that other vendors can independently implement.

Atlassian says AMP is live across its cloud platform, with capabilities such as displaying agent sessions in Jira and identifying agent-written content in Confluence. Dedicated agent accounts and centralized identity controls remain on the roadmap.

This distinction matters because making agent activity visible is a step toward accountability, whereas assigning ownership and controlling access require additional governance capabilities.

Atlaassian’s Teamwork Graph is the strategic core. It maps relationships among people, Jira work items, Confluence pages, service tickets, code, and conversations. Atlassian says it now holds more than 250 billion connections, up from roughly 150 billion six months ago. These are links among pieces of information, not a count of underlying records.

The value lies in what those links explain. A Jira ticket can identify a business requirement, a Confluence page can capture the rationale behind it, a code repository can record its implementation, and a Jira Service Management incident can show what happened after deployment. Connecting these records gives an agent a chain of context from the decision through execution to results.

At Team ‘26 Europe, Atlassian expanded both the graph and the ways agents access it.

The graph now indexes source code down to individual functions across Bitbucket and GitHub. It also ingests metadata, which describes data, from platforms such as Snowflake, Databricks, and BigQuery.

Coding agents can use the codebase alongside the business requirements that underpin it. Atlassian claims this combination reduces token consumption, the amount of information an AI model processes, which affects its operating cost.

A rebuilt Atlassian MCP server enables external agents to interact with Atlassian tools and data. It exposes more than 200 tools to agents, including Claude, ChatGPT, Codex, and Cursor.

Atlassian reports it sees more than 15 million MCP tool calls daily.

Agent sessions from Claude Code, Cursor, Codex, and Atlassian’s own Rovo now appear on Jira work items in real time. Confluence page history shows which sections a person wrote and which sections an agent wrote.

Atlassian plans to introduce dedicated agent accounts and an inventory of non-human identities, meaning identities assigned to software or agents rather than people. These are intended to give administrators a single place to manage and revoke agent access. Atlassian lists them as coming soon, without a specific date.

For executives, the practical goal is accountability. Visibility into agent sessions and authorship helps teams trace automated work when an agent changes a specification or proposes a code change. Planned identity controls would provide clearer ownership and access management.

Together, these capabilities could support audit and compliance work while helping organizations assess whether agent output justifies its cost.

The Battle For Enterprise Context

The broader significance of AMP extends beyond Atlassian’s agent management. As leading AI models become more competitive, platforms that hold valuable enterprise knowledge gain leverage in their relationships with model providers. The partnerships surrounding Team ‘26 Europe illustrate that dynamic.

Frontier Model Partnerships

On October 6, Atlassian and OpenAI expanded their relationship, introducing the GPT-6 family of models that power agents across Rovo and new connectors linking ChatGPT and Codex to Atlassian data.

Anthropic’s Claude is also integrated through Claude Agent for Jira, Claude model selection within Rovo, and Claude Code sessions tracked in Jira.

Working with both providers gives Atlassian access to competing sources of AI capability while making its context available to customers in either ecosystem. CEO Mike Cannon-Brookes reinforced that approach on stage by inviting customers to bring their preferred agent platform.

That arrangement makes the model providers both suppliers and competitors. OpenAI and Anthropic both sell enterprise products that connect to company data and aim to become the interface through which employees direct their agents.

Atlassian’s defense rests on the accumulated record of how its customers work, which is harder to reproduce than access to a model. By letting outside agents read its graph and return output via agent sessions and a new Artifacts app, Atlassian aims to keep that record growing across the tools employees choose.

Atlassian is not alone in pursuing the context layer, and each major platform vendor approaches the problem using the data it already has.

  • Microsoft grounds Copilot in Microsoft Graph data spanning email, meetings, and files, while Entra provides identity management. Together, these capabilities give Microsoft a broad footprint across everyday enterprise work.
  • ServiceNow anchors its AI agents and AI Control Tower in IT and enterprise workflow data, backed by strong governance credentials among CIOs.
  • Salesforce grounds Agentforce in customer data and uses Slack as the interface through which employees interact with Agentforce.
  • GitHub integrate s coordination of agent work directly into its code repository and pull-request workflow.

Atlassian’s differentiation is strongest where Jira and Confluence already serve as the system of record, particularly in software engineering. Their links among requirements, decisions, code, and incidents provide a durable advantage.

Its reach is less established in enterprise-wide identity management, where Microsoft Entra and ServiceNow play broader roles, and in sales, finance, and HR, where Atlassian’s own applications hold less of the relevant data. That makes interoperability essential to its ambitions beyond what its products already document.

Several open questions will determine whether Atlassian converts its position into a durable advantage. Agent identity controls, first discussed at its May conference, still lack a ship date. AMP’s unpublished specification makes its interoperability harder to assess than Anthropic’s MCP.

The potential value is substantial as agents take on more autonomous work, but the richness of the delivered context remains a practical test. Any graph also depends on the content and permissions customers have accumulated. Incomplete records, stale information, and poorly managed access can limit its usefulness.

Over the next 12 to 24 months, the context layer is likely to become a more contested part of enterprise software. Greater choice among leading AI models gives enterprises more flexibility and pressures model providers to differentiate. Vendors that provide useful records of how work gets done, with permission-governed access, could capture a growing share of the value.

Usage-based pricing offers one path to that revenue. Atlassian says Rovo credits will transition to usage-based billing in December 2026, aligning revenue more closely with billable agent activity.

For buyers, the economic question is which actions consume credits, including when they use third-party agents, and whether the resulting work justifies the charge.

For enterprise leaders, model selection remains important, but so does choosing which platforms should govern the knowledge those models rely on.

Buyers should evaluate whether a platform provides the context their agents need, whether that context can be shared across systems, and whether governance covers both outside agents and the vendor’s own agents. Model providers will continue competing on capability, cost, and performance; institutional knowledge is harder to replicate or replace.

Atlassian has a credible advantage wherever Jira and Confluence already document how work gets done. Extending that advantage across broader enterprise operations will depend on interoperability, governance, and the quality of the underlying information.

AMP and its knowledge graph make Atlassian’s ambitions clear. Its success, however, will ultimately be measured by how effectively enterprises turn that institutional knowledge into reliable automated work.