Atlassian Launches System to Coordinate and Accelerate Agentic Engineering

Grounded in the Teamwork Graph, new capabilities across Jira and DX bring context, governance, and measurement to the AI-native SDLC.

TEAM Anywhere/SAN FRANCISCO--(BUSINESS WIRE)--Atlassian Corporation (NASDAQ: TEAM), a leading provider of AI-powered collaboration and team productivity software, today announced new capabilities across Jira and DX to help engineering organizations adopt and scale governed agentic workflows across the AI software development lifecycle.

This launch addresses a critical enterprise scaling gap. According to Atlassian’s 2026 AI SDLC study, while 94% of engineering leaders report using AI, only 6% have the systems in place to scale it across the full SDLC. As engineering teams move from ad hoc agent sessions to always-on workflows, they lack the systems to coordinate what agents do, govern where they act, and measure whether the work actually improves delivery.

“The biggest bottleneck in AI software engineering isn't model intelligence, it's organizational context,” said Taroon Mandhana, Atlassian CTO, AI & Teamwork. “Enterprises need more than isolated sessions and one-off prompts. Jira has long been the system of record for how teams work. By extending that foundation to orchestrate agents alongside engineers, we’re giving teams a safe, measurable way to scale agentic workflows across the SDLC.”

Building on Atlassian’s vision for the AI SDLC, today’s announcement brings those systems together to translate AI experimentation into enterprise-wide impact. Recent analysis by DX found teams whose AI tools used the most Atlassian context shipped roughly 64% more per developer.

Grounding agents in a shared context layer

Agents fail when they lack institutional knowledge. Without context from architecture, roadmaps, and requirements, agents can produce functional code that still misses the mark. Today, Atlassian is announcing two new ways to ground agents:

• Code Context, built on Atlassian’s Teamwork Graph, gives Rovo and coding agents secure intelligence across multi-repository codebases. This enables more accurate results across the entire lifecycle, from vetting backlog ideas for architectural feasibility and generating code-aware implementation plans to accelerating bug triage and root-cause discovery.
• Agent Context Controls let platform teams govern which Jira and Confluence spaces agents can access, keeping outputs aligned with curated requirements, architectural decisions, and project standards.

Increasing engineering capacity with autonomous agent loops

To scale beyond individual tasks, teams need governed workflows for delegating work and keeping delivery moving.

New capabilities from Atlassian now turn engineering backlogs into always-on, automated execution cycles:

• Agent loops in Jira automate the path from backlog to pull request by continuously scanning for well-defined, unassigned work items, delegating them to Jira Coding Agent for execution and testing, and opening ready-to-review PRs directly in Jira.
• Standards enables platform teams to define organizational coding standards once and map them to repositories, automatically sharing consistent quality guardrails across every agent and developer operating in the codebase.
• AI review uses a dedicated agent to review pull requests against organizational standards, flagging issues before code ships.

Together, these capabilities transform asynchronous agent execution into a predictable pipeline. Agents operate in parallel, developers review and approve what ships, and teams gain more time for strategic work.

Making agentic work accountable and measurable

As agentic work scales, engineering organizations need automation, auditing, and measurement built into the systems teams already use.

• DX for Agentic Development measures AI impact across throughput, quality, adoption, and cost, mapping total AI investment directly to engineering outputs. It unifies AI Code Insights, tool and MCP tracking, model-to-task fit, and academic-validated Agent Experience (AX) research with rich software context and guardrails, closing the loop between AI observability and governance.
• Jira Agent Usage Dashboard helps team leaders understand which agents are used in their workflows, correlate agent sessions to Jira work items, and improve team delivery velocity with agents.

Together, these capabilities bring transparency and governance to autonomous work, enabling engineering leaders to continuously measure, optimize, and prove the business value of human-agent collaboration.

Learn more about Atlassian’s vision for the AI-native SDLC. And, join Atlassian on September 22, 2026 for the State of AI SDLC, a digital summit for engineering and product leaders exploring how AI is changing the way software gets planned, built, and run.

Availability

Code Context is gradually rolling out to paid Atlassian customers through open beta. Agent loops, Standards, and AI Review are available in private early access. Agent Context Controls and Agent Usage Dashboard will be generally available to paid Jira customers in the coming months. DX for Agentic Development will be generally available for Atlassian DX customers this quarter.

About Atlassian

Atlassian unleashes the potential of every team. A recognized leader in software development, work management, and enterprise service management software, Atlassian enables enterprises to connect their business and technology teams with an AI-powered system of work that unlocks productivity at scale. Atlassian’s collaboration software powers over 85% of the Fortune 500 and 350,000+ customers worldwide - including NASA, Rivian, Deutsche Bank, United Airlines, and Bosch - who rely on our solutions to drive work forward.

Arseny Tseytlin

press@atlassian.com