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Atlassian Automation Platform

7 posts · Jun 2020 – Jul 2026

What this trail covers

Atlassian Automation is a core platform capability across Jira, Confluence, and Jira Service Management, enabling users to automate repetitive tasks, streamline workflows, and integrate with third-party tools. It features a low-code/no-code visual rule builder, template libraries, and AI-driven rule creation. The platform integrates with Rovo AI Agents for advanced content analysis and manipulation. Key use cases include keeping content and work up-to-date, routing work to the right teams, and a new capability for custom automation steps built with Forge, allowing developers to add their own actions directly into the Automation rule builder. These Forge Automation Actions are configured via a UI provided by the Forge app, executed as Forge functions, and can call Atlassian APIs and external services. They support smart values for inputs and outputs, enabling dynamic and reusable automations.

All Atlassian trails
  1. From prompts to orchestration: Scale AI coding agent impact with Jira AutomationThis post details the expansion of Atlassian Automation to act as an open control plane for AI coding agents, integrating native support for GitHub Copilot, Cursor, and Claude. It describes how automation rules can now include AI agent invocation as an action step, triggered by events in Jira. The post provides examples of agentic automations for vulnerability patching, stale feature flag cleanup, and continuous bug triage, highlighting how structured context is passed to agents and outcomes are logged in Jira's audit trail. It emphasizes the ability to use different agents across teams and maintain centralized governance.
  2. Automate more in Jira: A guide to Forge Automation ActionsThis post introduces Forge Automation Actions, a new capability within the Atlassian Automation Platform. These actions allow developers to build custom automation steps using Forge, which are then integrated directly into the Jira Automation rule builder. Key technical aspects include: defining custom actions as Forge apps, executing them as Forge functions, enabling calls to Atlassian and external APIs, and supporting smart values for dynamic inputs and outputs. The post highlights the benefits of direct integration, ease of configuration, and reusability, contrasting them with other Forge automation patterns like 'Send web request'. It also provides links to a tutorial, a Forge Automation template, and documentation.
  3. How Atlassian Automation accelerates work across Confluence, Jira, and Jira Service ManagementThis post details the integration of Rovo AI Agents with Atlassian Automation, enabling AI-driven workflows for tasks such as analyzing content, creating content, and editing content. It highlights specific AI agents like the Product Requirements Document (PRD) Agent, Customer Feedback Agent, and Translation Agent, and emphasizes the platform's AI-driven rule creation capabilities. The post also reiterates core use cases of keeping content and work up-to-date, routing work to the right teams, and standardizing processes, now enhanced by AI capabilities.
  4. How K15t leverages Confluence Automation to manage content at scaleThis post details K15t's specific implementations of Confluence Automation for three use cases: 1) Copy-edit requests: manual automation triggering Slack notifications with context and due dates. 2) Weekly meeting notes: automated page creation and Slack reminders, followed by a separate automation to archive inactive pages based on a label and inactivity for seven days. 3) Content relevance check: monthly automation sending emails to page owners for inactive pages (no views, comments, or edits for over a month) to prompt review or archiving, with a trigger for another automation to archive. It also highlights the benefits of visibility, control, and the low-code builder for admins, and content organization and task automation for teams, including the use of smart buttons.
  5. The future of automation at Atlassian: Generating Confluence automation rules with large language modelsThis post details the technical implementation of generating Confluence automation rules using LLMs, specifically OpenAI's models with function calling. It breaks down the process into three steps: Selection (trigger and non-trigger component selection), Generation (rule structure generation based on selected components and prompt), and Mapping (resolving references to internal representations). The post highlights the benefits of splitting the generation process for context limits and cost savings, and discusses strategies for handling user intent and error cases.
  7. A modern approach to change management with Bitbucket Cloud and Jira Service ManagementThis post introduces the integration of Bitbucket Cloud and Jira Service Management for modern change management. It details how developers can create change requests directly from Bitbucket Pipelines, eliminating context switching and toil. It also highlights the risk assessment engine powered by Jira Automation for auto-approving low-risk changes and routing high-risk ones, and the complete audit trail provided in Jira Service Management for full traceability of deployed changes, including pipeline information and links to related Jira Software issues via smart commits. The post also mentions the ability to track deployments on a timeline to associate them with incidents.
  8. Automate collaboration - 12 new DevOps featuresThis post announces 12 new features and integrations focused on automating collaboration within the DevOps toolchain. Key contributions include: enhanced Jira Software Cloud and Bitbucket Cloud integrations for automatic issue tracking and project updates, new DevOps Automation Triggers in Jira for syncing work with development activities (commits, PRs), an improved Bitbucket Cloud Pull Request Experience, the Atlassian VS Code integration, Code Insights in Bitbucket Cloud for integrating scanning and quality reports (including Snyk for security), automated change management with Jira Service Desk Cloud and Bitbucket Pipelines (early access), and deep integration between Opsgenie, Jira Software Cloud, and Bitbucket Cloud for incident investigation dashboards.