Cursor is introducing Cursor Automations, a system designed to build and deploy autonomous agents that operate continuously within the software development lifecycle. These agents are triggered by a range of events, including Slack messages, GitHub pull request merges, or PagerDuty incidents, and can also be scheduled for routine tasks.
The core idea is to scale the often-lagging aspects of software development—code review, monitoring, and maintenance—to match the accelerated pace of code generation brought about by AI coding assistants. Each automated agent operates within a cloud sandbox, executing instructions and verifying its own output. A memory tool allows these agents to learn from past runs and improve over time.
Upgrading the Software Engineering Pipeline
As AI tools empower engineers to write more code, the bottleneck shifts to the processes that ensure code quality and stability. Cursor Automations aim to address this by enabling engineers to build a continuous improvement loop for their codebase, effectively creating a 'software factory'. This approach to software engineering pipeline automation allows for tasks to be handled proactively and consistently.
Review and Monitoring
Automations excel at code review, capable of catching everything from stylistic inconsistencies and security vulnerabilities to performance regressions. Cursor's own Bugbot, a long-standing automation, serves as an example, running on every PR to detect and fix bugs. The platform allows for customized review agents tailored to specific needs.
Cursor utilizes three key review automations: a security review agent that audits pushes to the main branch for vulnerabilities; an agentic codeowners system that assigns reviewers based on PR risk; and an incident response automation that investigates PagerDuty alerts by analyzing logs and code changes, then proposes fixes.
The security review agent works on pushes to main, allowing it more time to find complex issues without blocking immediate PR merges. It flags high-risk findings to Slack. The agentic codeowners system assesses PR risk and auto-approves low-risk changes, assigning reviewers for higher-risk ones. Decisions are logged for auditing. The incident response automation uses tools like Datadog to investigate alerts and automatically generates a PR with a proposed fix, significantly reducing resolution times.
Chores and Knowledge Work
Beyond code quality, automations streamline routine tasks and knowledge management. A weekly summary agent compiles significant repository changes into a Slack digest. Another agent reviews newly merged code each morning to identify and add missing test coverage, opening a PR with the new tests.
Bug report triage is also automated: agents check for duplicates, create issues, investigate root causes, attempt fixes, and summarize findings in the original thread.
Rippling's Automated Workflow
Companies outside Cursor are already adopting this approach. Abhishek Singh at Rippling developed a personal assistant agent that consolidates meeting notes, action items, and links from Slack, cross-referencing them with GitHub and Jira for a unified dashboard. Slack-triggered automations handle Jira issue creation and Confluence summarization.
Rippling teams also use automations for incident triage, status reports, and on-call handoffs. The company emphasizes that widely useful automations are shared across teams, freeing up engineers for more critical tasks. As Tim Fall, Senior Staff Software Engineer at Rippling, notes, "Anything can be an automation!"
The Software Factory Concept
Cursor Automations are powered by cloud agents that function as independent workers, capable of building, testing, and demonstrating their output. By configuring these agents, development teams can construct a robust 'software factory' for continuous code monitoring and improvement. Tal Peretz, Co-founder of Runlayer, highlights their experience, stating, "We move faster than teams five times our size because our agents have the right tools, the right context, and the right guardrails."
