I genuinely don't know how to feel about waking up, checking my team's Slack, and seeing that a critical bug was investigated, patched, and pushed to a PR at 3:30 AM. It is even weirder when I realize nobody actually stayed up to do it.
Cursor recently rolled out Automations. These are essentially always-on agents that run in the background. They listen for webhooks and run on cron schedules. Some even wake up when a PagerDuty alert fires. I have been watching teams start to use them, and the repository starts to feel like a living thing that cleans up after itself.
The bottleneck shifted
For the last year, coding assistants made us much faster at writing initial drafts of code. Everyone celebrated the productivity gains.
But here is the problem. If a team of five developers suddenly writes code like a team of twenty, the code review pipeline breaks. QA breaks. Security audits take too long. We sped up the assembly line, but the inspectors at the end could not keep up.
This is exactly why background agents make sense right now. We do not need help writing boilerplate anymore. We need help reviewing the massive volume of code our existing AI tools are spitting out.
The new security and operations shift
Cursor's own team uses these automations for the exact bottlenecks I just mentioned.
When they push to their main branch, an agent wakes up. It does not block the deployment, but it takes its time running a deep security audit on the diff. If it finds a high-risk vulnerability, it pings the team in Slack. They also have an incident response agent. If PagerDuty triggers an alert, the agent spins up, checks Datadog logs via MCP (Model Context Protocol), looks at recent code changes, and drafts a PR with a fix before a human even opens their laptop.
That last part is what gets me. The time between "system is broken" and "here is a proposed fix" is shrinking to seconds.
Cleaning up the mess
Then there are the chores. Nobody likes writing tests for code that already works.
You can set an agent to wake up every morning, look at all the code merged yesterday, figure out what lacks test coverage, and write the tests using your existing testing conventions. It just runs the tests, verifies they pass, and opens a PR.
They also handle bug triage. Someone drops a vague bug report in a Slack channel. The agent reads it, checks if it is a duplicate, creates a Linear ticket, searches the codebase for the root cause, and replies in the Slack thread. The amount of mental energy saved by not having to context switch for every minor bug report is massive.
It scales down to personal tasks
It is not just for massive codebases. A team over at Rippling started using these as personal assistants.
One engineer drops random meeting notes, Loom links, and ideas into a private Slack channel all day. Every two hours, an agent reads that channel, cross-references his GitHub PRs and Jira tickets, and posts a clean, deduplicated dashboard of exactly what he needs to care about.
He effectively built a custom executive assistant using a cron job and a few API connections.
The software factory
We are moving away from the idea of an IDE as just a text editor. We are building software factories.
When agents have their own isolated cloud sandboxes, access to your tools, and the ability to run on a schedule, they stop being tools you use. They become coworkers you manage. They handle the maintenance while you figure out what to build next.
Official Links
I am still getting used to the idea of background agents modifying my code while I am not looking. It requires a lot of trust. But after seeing how much busywork they eliminate, it feels inevitable. You can try setting one up yourself if you use Cursor, or just start thinking about which parts of your day could be handed off to a cron job with an LLM attached.
Try building a simple automation this week. Start with something low-stakes, like summarizing your open PRs, and see how it feels.