> For the complete documentation index, see [llms.txt](https://docs.helvia.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.helvia.ai/build/automations.md). # Automations Automations run an agent's workflows on a recurring schedule, without anyone starting a conversation. Use them to fetch external data on a schedule, post periodic updates, generate reports, or kick off any task an agent can already handle on its own. Open **Designer > Automations** to see every automation attached to the agent.
### What Automations Unlock Scheduling a workflow turns an agent from purely reactive into something that acts on its own clock. The same workflow you'd run inside a conversation can fire on a schedule or on demand, and every run is traceable back to the session it produced.
TitleDescription

:clock:

Schedule Recurring Runs

Pair any workflow with a day-of-week and time-of-day pattern

:play:

Trigger Workflows On Demand

Run any schedule immediately with Run Now, without waiting for the next slot

:eye:

Audit Every Execution

Inspect status, duration, and the underlying session for each run
### Creating an Automation {% stepper %} {% step %} #### Start a New Schedule Navigate to **Designer > Automations** and select **Add Schedule** to open the creation dialog. {% endstep %} {% step %} #### Choose a Deployment and Workflow Pick the **Deployment** that will host the run, then select the workflow to execute. {% hint style="info" %} Automations always run through an API deployment. Make sure the agent has one set up before scheduling. {% endhint %} {% endstep %} {% step %} #### Pick a Cadence Pick the days of the week and exact time the workflow should run on. The **Timezone** is your Workspace timezone and cannot be changed from the dialog. {% endstep %} {% step %} #### Save the Schedule Select **Create Schedule** to activate the schedule. It appears in the Automation table, active by default, and starts firing on its next scheduled slot. {% endstep %} {% endstepper %} ### Managing Automations The Automations tab is where you manage every automation on the agent. You can see which of them are active, which failed last time they ran, and when each one is due next. Search by workflow name to find one quickly, or filter by status to focus on what needs attention. From here you can pause an automation, run it on demand, or open it to change the days, time, or workflow it points to. Three actions are available for any automation:
TitleDescription

:pen:

Edit

Re-open the automation in the Edit Schedule dialog to change days, time, deployment, or workflow

:play:

Run Now

Trigger the automation immediately, without waiting for its next slot

:trash-can:

Delete

Remove the automation and stop all future runs
### Execution History Every automation keeps a record of all runs it has produced, so you can confirm whether it actually fired and investigate the ones that didn't. The history shows when each run happened, how long it took, and whether it succeeded. When something looks wrong, jump straight to the underlying session in Observatory to see what the workflow actually did.
#### Tracking Executions in Observatory Every execution links to the Observatory session it produced via the :eye: action button. Open the session to replay the workflow, inspect variables, and confirm the run did what you expected. Sessions from automations are tagged `AUTOMATION__` and recorded with the contact type `Scheduled Flow`. {% hint style="info" %} **Filter by automation:** Use the automation tag to isolate one schedule's history, or filter by the contact type to see every automation run across the agent in one view. {% endhint %} ### How Scheduled Runs Behave A scheduled run is not a live conversation. When the schedule triggers, the workflow runs without a user message to react to, so build it to do useful work on its own from the moment it starts. Apart from the missing user message, everything else works the same. Plugins, knowledge retrieval, LLM nodes, and integrations behave the same as in a conversation. Each execution can run for up to 30 minutes before the platform stops it. The default fits multi-step workflows that legitimately take minutes to complete. {% hint style="warning" %} **Exactly once on timeout:** If a scheduled run hits the timeout, the platform does not retry it. Each trigger produces exactly one execution, which keeps non-idempotent workflows (orders, tickets, payments) from running twice. {% endhint %} ### Best Practices * **Design for no user input:** Scheduled workflows start with an empty session. Make sure the first node knows what to do without a prompt. * **Test with Run Now first:** Validate the workflow once on demand before relying on the schedule * **Watch the Last Run filter:** Bookmark the `Failed` filter on Active schedules to spot drift early * **Pause instead of delete:** Toggling **Active** off preserves the schedule and its history, which is safer than deleting during incident response {% hint style="success" %} You can now schedule workflows to run on set days and times, trigger them on demand, and trace every execution back to the session that produced it. {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.helvia.ai/build/automations.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.