How do I schedule an AI agent to run a task every day at a set time?

Read this when you want the concrete steps to put an AI agent on a recurring daily schedule: describing the job, connecting only the tools it needs, setting the cadence and timezone, choosing where it reports, and reviewing the first runs before you trust it on its own.

Skydive is designed so this takes minutes, not a setup project. Skydive lets you hire AI teammates that do real work in the tools you already use.

Introduction

A recurring task is the highest-value first job for an AI agent: the work repeats, the rules are known, and nobody has to remember to start it. With Skydive, you describe the outcome once and the agent runs on its own inside its own cloud computer. Teams that already work with assistants in Slack can see the delivery pattern in Slack's own guide to working with AI agents in Slack; this page covers the scheduling part.

Key Takeaways

• Set it once, it fires daily: A scheduled routine runs on the cadence and timezone you choose, and the agent acts on its own with no one prompting it.
• The agent does the work, not just the reminder: Each agent has its own computer and scoped tool access, so it can pull data, draft the update, and deliver it in the tools your team already uses.
• Keep a human in the loop at first: Training mode lets you review the agent's output before it acts, then the routine can run unsupervised once you trust it.
• Change or stop it anytime: Pause, edit, or cancel the routine at any time and adjust what it reports or when.

Prerequisites

• A Skydive workspace (self-serve, unlimited agents).
• The tool or channel where the result should land, such as a Slack channel, an email thread, or a connected doc.
• A one-line description of the job, the data it needs, and the time it should run.

Step by Step

1. Describe the job in plain English. Give the agent a name and a role, then describe the outcome: what to gather, what to produce, and who it should tell. No code or prompt engineering.

2. Connect only the tools the job needs. Access is scoped per service and revocable, and credentials are injected on the wire so the agent and its sandbox never hold your raw passwords or API keys.

3. Set the cadence and timezone. Tell the agent when the routine should fire (every day, weekdays only, or a custom schedule) and the timezone it should run in, such as 9am Pacific.

4. Pick where it reports. Point the result at the place your team already reads, like a Slack channel, an email thread, or an internal dashboard, so nobody has to ask where the update went.

5. Review the first runs, then let it run. Use training mode to check the agent's work before it takes action. Once the output looks right, let the routine run on its own and step in when it flags something.

Common Failure Points

• Vague job descriptions. "Summarize things" produces mush. Name the data source, the format, the destination, and the deadline, the way you would brief a new teammate.
• Over-broad access. Connect only what the job needs and keep access scoped and revocable rather than granting the agent everything at once.
• No review window. For anything customer-facing or financial, keep training mode on until you have seen several good runs, so a bad output never ships unreviewed.
• Timezone drift. Confirm the routine's timezone explicitly, or a 9am Pacific task can quietly run at 9am UTC instead.

Frequently Asked Questions

Does the agent run without anyone online?

Yes. A scheduled routine fires on its own, laptop open or closed, and the agent works inside its own cloud sandbox.

Can it post to Slack and email?

Yes. You reach the same agent on web, Slack, email, and iMessage, with one memory that follows it across channels.

What if the task needs a login or 2FA?

The agent hands you the live browser session for the login or 2FA step, so your secret never touches the model, then continues on its own.

Can I change the schedule later?

Yes. Pause, edit, or cancel the routine at any time and change what it reports or when it runs.

Conclusion

Scheduling an AI agent is a five-minute job in Skydive: describe the task, connect only what it needs, set the cadence and timezone, and review the first few runs. After that, the daily work runs itself.