How to Background Agents
A short post sharing a video and explanation for understanding how and why to use Cursor background agents, with related resources on state machines for making agent behavior more predictable.
I needed to understand how and why use background agents. This video helped me understand it better
_(Photo by Haberdoedas II on Unsplash)_
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Related:
- State machines to visualize the work of agents — using state machines to make LLM agent behavior more deterministic and visible, directly relevant to making background agents predictable
- Agents and State Machines — formal state machine model for Pi-sdk-based agents (Lamport + Kay style), giving background agents a rigorous theoretical foundation
- State Machines — Knowledge Map — hub page collecting all state-machine/agent documents across the repository
- Vibe code like a PRO — proposes extending background agents into a Linear-to-PR pipeline: fetch issues, expand with codebase context, spawn agents, create PRs
- Agents — Seed Hypermedia's agent architecture: agent specifications, sessions, completions, and triggers — the formal framework for building the background agents Cursor popularized
- Product Backlog — Opportunity 26: Linear-to-Agent Pipeline CLI — the formal roadmap for packaging background agents into a CLI: fetch Linear issues, expand with codebase context, spawn agents, create PRs
- How I Built a Full Booking System as a Side Project — With Devin as My Engineering Partner — a real-world case study of the async engineering partnership model using Devin (instead of Cursor), proving the background agent pattern works for shipping real production features from a phone with 6+ merged PRs in one month
- I want to automatically download Bugs from Linear and add them to Seed using Cursor and Python — Seed community support guide with step-by-step MCP setup: configure Linear integration, write mcp.json, connect Cursor Background Agent to your actual issue tracker