> ## Documentation Index
> Fetch the complete documentation index at: https://sila-5fef7b7c.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Agents & automations

> Creating and managing workspace agents, and scheduling recurring automation runs.

Model discovery is available at **read** level. Creating or changing agents and automations requires **member** level and always acts on behalf of the authorizing user.

## Managing agents

* `sila_list_agent_models` returns every model currently available for Sila-hosted agents. Use the exact `id` from its `models` array when creating an agent. The catalog is shared across Sila, named agents, outside agents, external agents, and OAuth-connected agents.
* `sila_create_agent` creates a workspace agent: a `name`, standing instructions in `description`, the `channels` it works in, an optional per-agent `model`, integration `apps` (registry slugs like "gmail" or "linear"), `skillIds` from the workspace library, and `attachedFiles` as its knowledge base. Set `isExternal: true` for a locked-down agent that only works in external channels.
* `sila_update_agent` edits an existing agent. The authorizing user must be its creator, a workspace admin, or share a channel with it. Array fields (`channels`, `apps`, `skillIds`, `attachedFiles`) replace the full set rather than appending.
* `sila_toggle_agent` enables or disables an agent without deleting it. There is no delete via MCP; that stays with humans in the app.

## Select a model when creating an agent

Call `sila_list_agent_models` first with no arguments. Each returned model includes an `id`, display `name`, `provider`, and `category`. Pass the exact `id` to `sila_create_agent.model`:

```json theme={null}
{
  "name": "Research Agent",
  "description": "Research product questions and summarize the evidence.",
  "channels": ["research"],
  "model": "openai/gpt-5.6-terra"
}
```

The server rejects unknown model ids and points the caller back to `sila_list_agent_models`. Omit `model` to use the workspace default; agents using that default follow future workspace-level model changes.

## Automations

Automations are scheduled runs owned by the authorizing user. Three kinds, in increasing weight:

| Kind                | What runs                                             |
| ------------------- | ----------------------------------------------------- |
| `script`            | Python in a sandbox, printing a JSON result           |
| `conditional_agent` | Python gate that decides whether to fire an agent run |
| `full_agent`        | A full LLM agent run from your `agentPrompt`          |

* `sila_write_automation` creates or updates one. Required on create: `kind`, `name`, and `targetChannelIds`, the smallest set of channels the automation needs to post to (prefer exactly one). That set also scopes each run's read and post access, so keep it minimal. Schedule with 5-field cron in `cron` plus a `timezone`; leave `cron` null for a draft. Minimum cadence is every 5 minutes.
* `sila_test_automation` runs automation Python once in a sandbox right now, without scheduling anything. Test before you schedule.
* `sila_list_automations` (read level) shows your automations with status, last error, and next run time.
* Deleting an automation is not available over MCP. Disable it with `enabled: false`, or ask the user to delete it in the app.

## Create a workspace: `sila_create_workspace`

Creates a brand-new Sila workspace owned by the authorizing user, with default channels set up. Useful when you're helping someone start from nothing. Note that your current grant stays bound to the workspace picked at consent; the user reconnects to work in the new one.

For channel structure and invites, see [Workspace admin](/tools/workspace-admin).