No-Code Workflows (DAG Automation)
Last updated 8 Nov 2026
Overview
Workflows let you chain steps into an automated pipeline — a DAG (directed acyclic graph) of nodes — without writing code. A workflow can generate content, call AI capabilities, hit external APIs, branch, loop, wait, get approvals, and publish results. Each step runs with its own timeout, budget, and error policy so a single flaky step can't run away or hang the whole run.
Node kinds
Workflows support these step types:
- Capability — call an AI capability (chat, image, TTS, translate, etc.)
- Tool — invoke a registered tool/function
- HttpRequest — call an external HTTP endpoint
- Branch — route based on a condition
- Loop — iterate over a collection
- FanOut / FanIn — parallelise and rejoin
- Transform — set/reshape variables
- ArtifactSave — persist a transient asset to your CDN
- Publish — push to a channel (webhook, markdown artifact, etc.)
- SubWorkflow — call another workflow as a step
- HumanApproval — pause for an approval
- Wait — delay
- Webhook — POST to an external hook
Per-step controls
Each node carries:
- Timeout — how long the step may run before it's cut off.
- Error policy — what happens on failure: fail the run, retry, or continue/ignore. For example, set a notification step to ignore so an outage there doesn't fail the whole run, and set a fetch step to retry for transient upstream blips.
Variables and artifacts
- Variables thread data between steps. Steps read prior outputs with
{{vars.name}}interpolation. - Artifacts are outputs you persist — ArtifactSave re-hosts transient provider URLs to permanent Bunny CDN URLs so links don't expire.
Cost
There is no separate workflow execution fee. A run is billed as the sum of every step's BillingRecord — exactly the same per-call billing as running each step by hand. Steps that make no AI call (HTTP, transform, wait, webhook) cost nothing. Sub-workflow costs roll up into the parent run's total.
Running and monitoring
Kick off a run, then watch it on the workflow runs page. Each run records its steps, their outcomes, and total cost. Failed steps surface their error per the step's error policy.
Tips
- Give external/notification steps a forgiving error policy (retry or continue) and core steps a strict one.
- Use ArtifactSave on any generated media you want to keep — provider URLs expire.
- Extract reusable subgraphs into a SubWorkflow and call it from multiple parents.
- Test a flow with cheap echo/transform steps before wiring in costly AI capability steps.
Watch how it works
How AI workflows work: one form in, a finished video out
A workflow is a ready-made recipe of AI steps. You pick a template, fill in a short form, and every step runs on its own until a finished video comes out. Here we follow one bedtime story video from the form to the result.
What happens when you press Run? The anatomy of an AI workflow
Let's open one workflow and look inside. A workflow is a graph: each box is a step, the arrows set the order, and steps that don't depend on each other run side by side. Your answers flow into the steps, some steps check their own work and run again, some wait for your approval, and every AI call is recorded with its own cost.
The building blocks of an AI workflow
Every workflow, from a bedtime story to a research report, is built from the same few blocks. The AI step sends a prompt to a model and gets back text, an image, a voice, music or a video. Knowledge blocks bring in facts with their sources. Quality blocks check the work, and a human approval waits for you. Logic blocks shape the flow with branches and loops. Output blocks join everything into one video and save the result.