Connect the work between your apps.
Define webhook integrations, scheduled tasks, and multi-step processes in clean, declarative YAML. Your AI agent can easily build and modify backend pipelines while you inspect every run, input, and output.
IN PRACTICE
Publish a daily market brief on schedule.
A cron trigger fetches prices, selects the required JSON fields, and posts the finished brief to the application, with each step recorded in the run.
workflow_id: daily-market-brief
enabled: true
triggers:
- type: cron
cron: "0 13 * * 1-5"
steps:
- step_id: fetch-prices
kind: http
command:
method: GET
url: https://api.example.com/quotes
- step_id: publish-brief
kind: http
dependencies: [fetch-prices]
command:
method: POST
url: https://dashboard.example.com/api/market-briefsHOW IT WORKS
Describe the trigger and steps, then inspect every run.
- 01
Write a workflow manifest or ask the AI agent to create one.
- 02
Use triggers and steps for HTTP calls, waits, transforms, and approvals.
- 03
Deploy and inspect workflow runs from the platform.
WHY IT BELONGS HERE
Operational scripts become invisible until they fail.
Cron jobs, webhook transforms, approval steps, and maintenance tasks are often scattered across scripts and services with no shared run history.
Keep automation beside the app
Version triggers, shared context, secret references, and steps with the source they support.
Compose work from visible steps
Call HTTP services, transform data, wait, run code, query data, or introduce an approval.
Understand what happened later
Inspect each step instead of reconstructing a failed script from scattered logs.
What’s included
- ✓ Webhook, event, and schedule triggers
- ✓ HTTP steps with secret references
- ✓ Approval and wait steps
- ✓ Workflow logs and run state
Turn the next backend script into a workflow you can inspect.
Keep its trigger, steps, secrets, status, and output together long after the original request has finished.
CONNECTED CAPABILITIES
Keep building.
Managed Databases
Add Postgres, pgvector, Redis, MongoDB, MySQL, or RabbitMQ to your stack with one click. Adios injects secure connection details into the workspace, allowing your AI agent to instantly leverage caches, queues, and vector storage.
Explore →02 /Secrets & Environment Variables
AI agents can easily compromise hardcoded API keys and secrets. Adios keeps your credentials safe by replacing them with secret:// references in adios.yaml. Your agent can configure environment needs without ever seeing actual production values.
Explore →03 /Observability & Logs
When a build fails, your agent needs direct feedback. Adios links build logs, runtime streams, and health checks directly to the workspace, allowing your AI agent to independently diagnose, fix, and verify its own code changes.
Explore →