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

# Automations

> Multi-step, replayable analytics workflows

<RewriteBanner />

An **Automation** is a series of tasks (`tasks`) executed sequentially. Each
`task` is either a deterministic command (such as `execute_sql`, which runs a
named SQL query) or an agent given a prompt. Tasks compose by passing results
from one to the input of another — the output of each task is accessible with
Jinja as `{{ name_of_task }}`.

You build, run, and replay Automations from the Oxygen IDE. Each Automation is
stored as an `.automation.yml` file (the older `.procedure.yml` extension is
still recognized for back-compat).

## Automation components

Automations are DAGs comprised of `tasks`. Each task has a few common
properties:

| Component | Description                                                                           | Type     |
| --------- | ------------------------------------------------------------------------------------- | -------- |
| name      | Identifier for the task. Output of the task can be referenced as `{{name}}`.          | required |
| type      | The tool to use for this task. See [Task types](/docs/guide/build/automations/task-types). | required |

Specific task types have additional property requirements — see
[Task types](/docs/guide/build/automations/task-types) for the full set.

## A complete example

```yaml workflows/monthly_report.automation.yml theme={null}
tasks:
  - name: raw_sql_calculation
    type: execute_sql
    sql_file: month_over_month_overall_performance.sql # A sql file to execute deterministically
  - name: month_over_month_metrics # An identifier for the task. Results can be referenced by jinja-wrapping this name
    type: agent
    agent_ref: default # The agent .yml file to use for this task
    prompt: Calculate month-over-month performance of views and clicks for the entire ad portfolio. # The prompt given to the agent
  - name: monthly_report
    type: agent
    agent_ref: local
    prompt: |
      Create a report using the provided data that looks as follows:
      The overall portfolio brought in X views and Y clicks in MM/YYYY, up A% and B%, respectively.
      {{month_over_month_metrics}}
```

## Automations vs. chains

An Automation is similar to a "chain" in the prompt-engineering parlance, but
with a few key differences:

* **Automations are DAGs.** Whereas chains can become arbitrarily complex with
  arbitrarily nested loops, complex reply logic, and opaque branching structures,
  Automations are DAGs, which enforce a clearer, more predictable flow from input
  to output.
* **Automations separate logic from execution.** Because Automations are written
  in YAML, the DAG definition is entirely separate from the execution engine,
  while other code-based systems keep these tightly coupled and so ultimately
  become difficult to build and maintain.

These choices generally reduce the flexibility of Oxygen when compared to say,
`langchain` or `llama_index`, but they also dramatically reduce the complexity of
the system. You can think of Oxygen's Automation paradigm as a domain-specific
chain-builder for data workflows, where most (if not all) tasks simply pass
results around between different agents.

## Next steps

<CardGroup cols={3}>
  <Card title="Task types" icon="diagram-subtask" href="/docs/guide/build/automations/task-types">
    `agent`, `execute_sql`, `formatter`, and sub-automations
  </Card>

  <Card title="Loops" icon="repeat" href="/docs/guide/build/automations/loops">
    Iterate over values with `loop_sequential`
  </Card>

  <Card title="Variables" icon="brackets-curly" href="/docs/guide/build/automations/variables">
    Parameterize an Automation with typed inputs
  </Card>
</CardGroup>