Build agents that run automatically

2026-03-05 · Source: Cursor Blog · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

Cursor is introducing Cursor Automations, a new feature enabling the creation of "always-on" agents designed to automate various software engineering tasks. These agents can be scheduled or triggered by events from integrations like Slack, Linear, GitHub, and PagerDuty, or via custom webhooks. When activated, an agent spins up a cloud sandbox, executes instructions using configured MCPs and models, verifies its output, and can learn from past runs using a memory tool. Cursor has identified two primary categories for these automations: review and monitoring, which includes security reviews, agentic codeowners for PR risk classification, and incident response; and chores, covering tasks like weekly change summaries, test coverage identification, and bug report triage. Rippling, for example, uses these automations for personal assistants, Jira issue creation, and incident triage, demonstrating their utility in scaling development lifecycle processes beyond just code production.

Key takeaway

For engineering leaders aiming to scale their software development pipeline beyond just code generation, Cursor Automations offer a compelling solution. You should explore implementing these always-on agents to automate critical, repetitive tasks such as security reviews, PR management, incident response, and routine knowledge work. This approach can significantly reduce manual overhead, improve consistency, and free your engineers to focus on more complex, high-value activities, ultimately accelerating your team's velocity and reducing operational risks.

Key insights

Automated agents can significantly enhance software development pipelines by handling review, monitoring, and routine tasks.

Principles

• Automate repetitive tasks to free human engineers.
• Agents improve with repetition and access to memory.
• Integrate agents across the entire development lifecycle.

Method

Configure agents to run on schedules or event triggers (e.g., PRs, incidents). Agents use a cloud sandbox, MCPs, and models to execute instructions, verify output, and learn from past runs.

In practice

• Implement security review agents on every push to main.
• Automate PR risk classification and reviewer assignment.
• Use agents for incident response and proposed fix generation.

Topics

• AI Agents
• Software Automation
• MLOps
• Code Review
• Incident Response

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Software Engineer, MLOps Engineer, AI Engineer

Related on AIssential

• How AI Agents Can Automate Complex Tasks — AI agents automate complex, multi-step tasks by dynamically planning, reasoning, and adapting with minimal human oversight.
• 10 AI Automation Ideas That Actually Help Developers Build Faster — AI's true value in development is removing "invisible friction" and repetitive tasks, not just smarter autocomplete.
• 😺 🎙️ We're LIVE right now to demo a robot! — AI agents are transforming software development and robotics by automating complex tasks and enhancing code quality.
• How I Built an Autonomous AI Agent Team That Runs 24/7 — Autonomous AI agent teams can manage repetitive tasks, freeing human effort for creative work.
• The Agentic AI Shift Nobody Warned You About — AI coding tools are evolving into autonomous agents that plan and execute tasks without continuous human input.
• Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI — AI agents are fundamentally transforming software development and research by enabling extensive task delegation and automation.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Cursor Blog.