> ## Documentation Index
> Fetch the complete documentation index at: https://docs.runalph.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Automate Agents on a Schedule

> Run AI agents automatically on a recurring schedule

## Why

Combine AI with automation. Instead of pre-written notebook cells, give an AI agent a prompt and a schedule. The agent decides what to do each run — adapting to new data, changing conditions, or evolving requirements.

## Setup

<Steps>
  <Step title="Go to Automations">
    In your project, click **Automations** → **New Automation**
  </Step>

  <Step title="Choose Agent">
    Select **Agent** as the automation type
  </Step>

  <Step title="Configure">
    * Write a prompt describing what the agent should do
    * Select an AI model (Claude, GPT, or Gemini)
    * Set a name and cron schedule
    * Click **Create**
  </Step>
</Steps>

## Example Prompts

**Daily data summary:**

> Analyze today's sales data in sales\_2024.csv. Write a summary to daily\_reports/ with key metrics and any anomalies.

**Code health check:**

> Run the test suite. If any tests fail, investigate the failures and create a report in debug\_logs/.

**Research digest:**

> Check the latest papers on arxiv related to diffusion models. Summarize the top 3 in a new markdown file.

## How It Works

The agent gets your prompt, access to the project's filesystem and kernels, and runs autonomously. It can create files, execute code, read outputs, and iterate — just like an interactive IDE session, but unattended.

## Managing

* **Toggle** automations on/off
* **View run history** to see what the agent did, including generated files and outputs
* **Edit** the prompt, model, or schedule anytime

<Note>
  Agent automations use AI tokens billed to your organization. The project must be running for automations to execute. Default timeout is 300 seconds.
</Note>