What is Cursor Automation? (Cursor OpenClaw)

TL;DR

Cursor Automation is a cloud-based agent system that runs AI-powered workflows automatically on schedules or when triggered by events such as Slack messages, GitHub pull requests, Linear issues, or PagerDuty incidents. Unlike chat-based AI assistants, Cursor Automations run in the background, creating cloud sandboxes to review code, monitor systems, handle chores, and respond to incidents without manual intervention. Teams can use Cursor Automations alongside tools such as Apidog to automate API testing, security reviews, and documentation updates.

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What Is Cursor Automation?

Cursor Automation lets engineering teams deploy AI agents that run on demand, on a schedule, or in response to events.

Instead of opening a chat window and asking an assistant to perform a task, you configure an agent with:

• A trigger
• Access to a repository or external tools
• Instructions and guardrails
• Verification steps
• An output destination

Traditional AI assistants wait for a prompt. Automations monitor your codebase, run tests, update documentation, and investigate incidents while your team focuses on feature development.

For API teams, Cursor Automations can complement Apidog. Apidog handles API design, testing, and documentation, while Cursor Automations can trigger test suites after deployments, monitor endpoint health, and update API documentation when code changes.

Why Cursor Built Automations

Cursor created Automations to address an internal bottleneck: AI coding agents increased development speed, but code review, monitoring, and maintenance did not scale at the same pace.

The Cursor team began using automated agents for tasks such as:

• Reviewing pull requests
• Running security checks
• Investigating incidents
• Updating documentation
• Handling routine maintenance

The company reports that its Bugbot automation runs thousands of times daily on pull requests and has caught millions of bugs. Its security review automations identify vulnerabilities asynchronously, while incident response agents investigate issues automatically.

Cursor later productized these internal workflows for other teams.

How Cursor Automations Work

A typical automation follows this sequence:

Event trigger
    ↓
Cloud sandbox
    ↓
AI agent
    ↓
Verification
    ↓
Output

1. Event Triggers

Triggers determine when an automation starts. Common examples include:

• A GitHub pull request is opened or updated
• A message appears in a specific Slack channel
• A Linear issue is created
• A PagerDuty incident is triggered
• A scheduled time is reached
• A custom webhook is called

2. Cloud Sandbox

Cursor creates an isolated environment with the code, tools, MCP integrations, and credentials required by the automation.

This gives the agent a controlled workspace in which it can:

• Read repository files
• Run shell commands
• Execute tests
• Make API calls
• Interact with external services

3. AI Agent

The agent follows your instructions and uses configured tools, including Model Context Protocol (MCP) integrations.

For example, an incident response agent might use:

• A Datadog MCP to retrieve logs and metrics
• A GitHub integration to inspect recent changes
• A Slack integration to notify the on-call engineer
• A repository workspace to create a proposed fix

4. Verification

The agent validates its work before producing an output. Depending on the workflow, verification might include:

• Running unit or integration tests
• Checking generated files
• Validating API responses
• Reviewing the final diff
• Confirming that required tools completed successfully

Verification reduces the risk of producing incomplete or broken changes, but it does not replace branch protection or human review.

5. Output

The result can be delivered through a configured destination:

• A Slack message
• A Linear issue
• A GitHub pull request
• A committed change
• A database record
• A documentation update

Memory and Learning

Cursor Automations include a memory tool that allows agents to retain lessons from previous runs.

For example, if a security review repeatedly reports a known false positive, the automation can store that pattern and avoid raising the same unnecessary alert in future runs.

Treat memory as a workflow aid rather than a replacement for explicit instructions. Important rules, severity thresholds, and approval requirements should remain in the automation configuration.

Two Main Categories of Automations

Most engineering automations fall into two categories:

1. Review and monitoring
2. Chore automation

Review and Monitoring

These automations inspect changes, identify issues, and monitor system behavior.

Typical characteristics:

• Triggered by code changes or schedules
• Analyze diffs, security risks, or performance
• Post findings to Slack or pull request comments
• Often run asynchronously instead of blocking merges

Chore Automations

Chore automations handle routine coordination work across multiple tools.

Typical characteristics:

• Run daily, weekly, or in response to events
• Aggregate information from several systems
• Create summaries, reports, or documentation
• Reduce manual coordination

Review and Monitoring Automations

Security Review Automation

A security review automation audits changes for potential vulnerabilities after code is pushed to the main branch.

A typical workflow:

1. Trigger when code is pushed to main
2. Analyze the changed files
3. Ignore concerns already discussed in the pull request
4. Post high-severity findings to a security Slack channel
5. Store findings for audit purposes

This approach runs asynchronously, so it does not necessarily slow down the development workflow while still surfacing issues quickly.

Example alert:

Security Alert: SQL Injection Risk

File: src/api/users.ts
Line: 47
Severity: HIGH

Query uses string concatenation with user input:
const query = `SELECT * FROM users WHERE id = ${userId}`;

Recommendation: Use parameterized queries:
const query = 'SELECT * FROM users WHERE id = ?';

PR: github.com/company/repo/pull/142

Agentic Codeowners

An agentic codeowners automation classifies pull requests by risk and assigns reviewers accordingly.

A workflow might:

1. Run when a pull request is opened or updated
2. Analyze changed files and their impact
3. Classify the pull request as low, medium, or high risk
4. Approve low-risk changes when configured to do so
5. Assign one or two reviewers to higher-risk changes
6. Post the decision to Slack and log it to Notion

This makes review policies more consistent. A documentation change may need less scrutiny than an infrastructure or database migration change.

Incident Response Automation

An incident response automation investigates PagerDuty incidents and prepares context for the on-call engineer.

A typical workflow:

1. Start when PagerDuty creates an incident
2. Retrieve relevant logs through Datadog MCP
3. Search the repository for recent changes
4. Identify a likely root cause
5. Create a pull request with a proposed fix
6. Send the investigation summary to Slack

Example output:

Incident Response: API Latency Spike

Monitor: Production API p95 > 2s
Started: 2:47 AM UTC
Affected endpoints: GET /api/users, POST /api/orders

Investigation complete:
- Database connection pool exhausted
- Root cause: Missing connection release in orderService.create()
- Changed in commit abc123 (deployed 2:30 AM)

Proposed fix: github.com/company/repo/pull/156
- Adds connection release in finally block
- Tested against staging database

On-call: @engineer-name
Reply 'deploy' to merge and deploy fix.

The value is in reducing investigation time. Instead of starting with an empty incident channel, the engineer receives an initial hypothesis, supporting context, and a proposed next step.

Chore Automations

Chore automations handle routine work that keeps teams aligned but consumes engineering time.

Weekly Summary of Changes

A weekly summary automation posts a Slack digest describing meaningful repository changes from the previous seven days.

It can include:

• Major merged pull requests
• Bug fixes and their impact
• Technical debt addressed
• Security and dependency updates
• New features shipped

Example output:

Weekly Engineering Summary (Mar 2-6)

Shipped Features:
- User preferences API (PR #134)
- Payment webhook integration (PR #141)
- Dashboard analytics v2 (PR #138)

Bug Fixes:
- Fixed race condition in order processing (PR #145)
- Resolved memory leak in WebSocket handler (PR #149)

Technical Debt:
- Migrated from Moment.js to date-fns (PR #142)
- Removed deprecated API endpoints (PR #150)

Security Updates:
- Updated lodash to 4.17.21 (CVE-2021-23337)
- Rotated database credentials

PRs Merged: 23
Lines Changed: +4,521 / -2,103

Test Coverage Automation

A test coverage automation reviews recently merged code and opens pull requests for missing tests.

A daily workflow might:

1. Run at 6 AM
2. Scan code merged during the previous 24 hours
3. Identify functions without tests
4. Generate tests that follow existing project conventions
5. Run the test suite
6. Open a pull request with the new tests

Keep this automation scoped to test files and require review before merging generated changes.

Bug Report Triage

A bug triage automation processes reports posted in Slack.

It can:

1. Monitor a #bug-reports channel
2. Search existing Linear issues for duplicates
3. Create a new issue when no duplicate exists
4. Inspect the codebase for a likely root cause
5. Attempt a fix and run tests
6. Reply in the Slack thread with a summary and pull request

Automating the initial investigation lets engineers spend more time fixing bugs and less time categorizing and reproducing them.

Real-World Examples from Teams

Rippling: Personal Assistant Dashboard

Abhishek Singh at Rippling built a personal assistant that aggregates tasks from multiple sources.

The workflow uses:

• A Slack channel for meeting notes, action items, TODOs, and Loom links
• A cron automation that runs every two hours
• Slack, GitHub pull requests, Jira issues, and Slack mentions as inputs
• Deduplication across sources
• A dashboard posted back to Slack

Additional automations include:

• Creating Jira issues from Slack threads
• Summarizing Confluence discussions
• Triaging incidents
• Generating weekly status reports
• Preparing on-call handoff documentation

Singh reports that these automations handle repetitive coordination work and leave more time for higher-impact tasks.

Runlayer: Software Factory

Runlayer built a software delivery pipeline using Cursor Automations, Runlayer MCP, and plugins.

Their approach uses:

• Cloud agents that monitor and improve the codebase
• Tools and context appropriate to each workflow
• Guardrails around agent execution
• Custom MCP integrations for their environment

The broader lesson is that automations can support both small scheduled tasks and larger workflows. A simple report may require only a schedule and instructions, while a software delivery workflow can combine repositories, webhooks, MCPs, and approval steps.

Cursor Automation vs. Other AI Tools

Feature	Cursor Automations	GitHub Copilot	ChatGPT/Claude Web	OpenClaw
Execution model	Automatic and scheduled	IDE autocomplete	Manual chat	Self-hosted chat
Triggers	Events, schedules, webhooks	Typing in an editor	User messages	User messages
Cloud or local	Cloud sandbox	Cloud	Cloud	Local on your machine
Integrations	Slack, GitHub, Linear, PagerDuty	IDE-focused	Browser-focused	Messaging apps
Memory	Persistent across runs	Session-based	Session-based	Local storage
Verification	Can self-check before committing	None by default	Manual	Depends on configuration

When to Use Cursor Automations

Cursor Automations are a good fit when you need:

• Work to happen without a manual prompt
• Integration with tools such as Slack, Linear, and GitHub
• Scheduled or event-driven execution
• Cloud execution in isolated sandboxes

When Other Tools Make More Sense

Use GitHub Copilot for:

• Real-time code completion
• Inline suggestions inside an IDE

Use ChatGPT or Claude for:

• One-off questions
• Explanations
• Brainstorming and exploration

Use OpenClaw for:

• A self-hosted personal assistant
• Messaging app integration such as WhatsApp or Telegram
• Local data privacy requirements

Who Should Use Cursor Automations?

Engineering Teams With Five or More Developers

Larger teams often face coordination overhead around reviews, status reporting, and incidents.

Good starting points:

• Agentic codeowners for pull request routing
• Weekly summaries for stakeholder updates
• Incident response for on-call support

DevOps and Platform Teams

Platform teams can use automations for monitoring and incident response.

Good starting points:

• PagerDuty incident investigation
• Scheduled health checks
• Dependency update workflows

API Development Teams

API teams can automate testing, monitoring, and documentation.

Good starting points:

• Post-deployment API test execution with Apidog
• API documentation updates when endpoints change
• Endpoint monitoring with targeted alerts

Security Teams

Security teams can use asynchronous checks to continuously audit changes without blocking every pull request.

Good starting points:

• Security reviews on the main branch
• Dependency vulnerability scanning
• Secret detection in pull requests

Solo Developers

Solo developers can use automations to handle maintenance work that competes with feature development.

Good starting points:

• Test coverage automation
• Bug report triage
• Weekly progress summaries

Getting Started With Cursor Automations

Before creating an automation, define the task in terms of its trigger, inputs, actions, verification, and output.

Requirements

You typically need:

• A Cursor account on a paid tier
• Access to a GitHub repository
• Slack workspace administrator access for Slack integrations
• API credentials for services such as Linear or PagerDuty

Setup Steps

1. Open the Automations Dashboard

Navigate to the Automations page on the Cursor website and sign in.

2. Choose a Template

Start with a template for a common workflow, such as:

• Security review
• Test coverage
• Weekly summary
• Incident response

Templates provide initial instructions and trigger configuration.

3. Configure the Trigger

Choose how the automation starts:

• Connect a GitHub repository for pull request events
• Add a Slack webhook for message events
• Configure a cron schedule
• Add a custom webhook

Keep the initial trigger narrow. For example, start with pushes to one branch or messages in one channel.

4. Configure MCPs and Tools

Add only the integrations the automation requires.

Examples include:

• Linear MCP for issue management
• Datadog MCP for logs and metrics
• Custom MCPs for internal tools

Limit credentials and permissions to the smallest useful scope.

5. Write Specific Instructions

Good instructions define:

• What the agent should inspect
• What it should create or change
• How to handle edge cases
• Where to publish results
• Which conditions require human input
• What it must never do

For example, specify whether the agent may open a pull request but not merge it.

6. Test the Automation

Run a test execution and verify that:

• The trigger fires correctly
• The agent follows the instructions
• Tool calls succeed
• Results reach the expected destination
• Errors are reported clearly
• Generated changes pass the configured checks

7. Monitor and Iterate

Review the first several executions. Then adjust:

• Instructions based on the output
• Memory for recurring patterns
• Trigger conditions
• Tool permissions
• Escalation thresholds

Example: Security Review Automation

Automation name: Security Review

Trigger: Push to the main branch

Instructions:

1. Analyze the code diff for security vulnerabilities.
2. Focus on SQL injection, XSS, CSRF, authentication bypass, and exposed secrets.
3. Skip issues already discussed in pull request comments.
4. For high-severity findings:
   • Post to the #security-alerts Slack channel.
   • Include the file path, line number, severity, and remediation.
5. Log all findings to a Notion database through MCP.
6. Do not merge or deploy changes.

Required MCPs:

• Slack MCP for alerts
• Notion MCP for audit logging

Models:

• Use Claude Sonnet for analysis.
• Fall back to GPT-4 if unavailable.

Best Practices

Start With High-Value, Low-Risk Automations

Begin with read-only or easily reversible workflows:

• Weekly summaries
• Bug triage that creates issues but does not merge code
• Test coverage pull requests

After validating the workflow, expand to security reviews, incident response, and other higher-impact tasks.

Use Asynchronous Execution for Reviews

Blocking reviews can slow down delivery. Run review automations after merges or in parallel with normal development, then post findings asynchronously.

Use branch protection and required reviews for automation-created pull requests.

Define Clear Escalation Paths

Give each severity level an explicit action:

• High severity: alert Slack immediately
• Medium severity: log for next-business-day review
• Low severity: include in a periodic summary

This prevents the agent from treating every finding as an emergency.

Build Memory Carefully

Store lessons from recurring mistakes, but keep critical policies in the automation instructions. Review memory entries periodically to ensure they remain accurate.

Combine Cursor Automations With Apidog for API Workflows

For API teams, a combined workflow can cover the full API lifecycle:

• Design and test APIs in Apidog
• Trigger Apidog test suites after deployments
• Monitor endpoint health
• Update API documentation when endpoints change
• Generate changelogs from project history

Cursor Automations coordinate the workflow, while Apidog provides the API design, testing, and documentation capabilities.

Document Your Automations

Maintain a central record that includes:

• Active automations
• Purpose and trigger for each automation
• Required tools and credentials
• Expected outputs
• Troubleshooting steps
• Owner or contact for changes

Monitor Automation Performance

Track whether each automation delivers measurable value:

• Time saved per week
• Issues caught before production
• False-positive rate
• Number of successful and failed runs
• Team satisfaction

Adjust or retire workflows that do not provide clear benefits.

What About Agents Outside Cursor?

Assigning a task to an agent or a crew in Sharkly

Cursor Automations are designed for workflows inside Cursor. If your team also uses Claude Code, Codex, or another CLI agent, the automation layer may stop at the boundary of the vendor you selected.

Sharkly approaches the problem from a work-management perspective. A task can be assigned to an Agent or a Crew, and execution happens on connected Computers using the runtimes installed there. Custom ACP runtimes can be added from the Computer detail page, allowing tools outside the built-in list to receive work.

The workflow concepts are similar:

• Automations live in a Space
• Repository runs use an isolated worktree per task
• Run states move from queued to completed or failed
• Execution logs record tool calls and trigger sources
• Agent output appears as task comments
• The Inbox separates work that needs human review

This is not an argument against Cursor Automations. It addresses the next question teams often have: how to run the same review and chore workflows when different developers use different agents.

FAQ

Is Cursor Automation Included in My Cursor Subscription?

Cursor Automations are available on paid Cursor plans. Check cursor.com/automations for current pricing and usage limits.

Can Cursor Automations Access Private Repositories?

Yes. You grant repository access during setup. Automations run in isolated cloud sandboxes with the access you provide.

How Do I Prevent Automations From Making Unwanted Changes?

Require approval before merging. Most teams begin with read-only workflows, then enable write access gradually as they validate the results.

Use branch protections and required reviews for automation-created pull requests.

What Happens If an Automation Introduces a Bug?

Automations can run tests before committing changes, but tests do not catch every bug. Keep normal review and deployment controls in place.

Can I Use Cursor Automations With Self-Hosted GitHub?

Cursor Automations support GitHub Enterprise Server. Configuration requires additional setup for webhook endpoints.

How Do Automations Handle API Rate Limits?

Automations respect rate limits from integrated services. For high-volume workflows, caching or batching requests may help reduce usage.

Can Multiple Team Members Share Automations?

Yes. Automations are team resources. Members can view, edit, and create automations according to their permissions.

What Is the Difference Between Cursor Automations and Zapier?

Zapier connects applications through predefined actions. Cursor Automations use AI agents that can reason about more complex tasks, make decisions, and adapt their actions based on the available context.

Do Automations Work With Monorepos?

Yes. Automations can analyze monorepos and determine which services are affected by a change. Configure paths and instructions to scope an automation to specific services.

How Do I Debug a Failing Automation?

Review the execution logs. They show the steps the automation took and can help identify whether the problem came from the trigger, instructions, tool integration, credentials, or verification step.

Conclusion

Cursor Automations shift repetitive engineering work from manually triggered assistants to always-on, event-driven workflows.

A practical implementation starts small:

1. Choose a repetitive task.
2. Use a narrow trigger.
3. Give the agent only the required tools and permissions.
4. Add verification steps.
5. Send results to a team-visible location.
6. Require human approval for high-impact changes.
7. Review execution logs and iterate.

Cursor's internal automations are reported to catch bugs, support incident response, and reduce coordination work. Teams such as Rippling and Runlayer have applied similar patterns to dashboards, software delivery, triage, and reporting.

For API development teams, Cursor Automations and Apidog can work together across the API lifecycle. Apidog handles API design, testing, and documentation, while Cursor Automations coordinate triggers, monitoring, and updates. The result is a workflow with fewer manual handoffs and clearer verification points.