AI workflow automation
Describe the outcome in a sentence and the AI plans, builds, and test-runs the workflow on real data. What it can build with and how verification works.
AI workflow automation means describing the outcome you want ("when someone submits my lead form, score the lead and Slack me the hot ones") and getting a working, tested workflow instead of a blank canvas. In MadMax that's the default way to build, not a bolt-on assistant.
What makes it different from a chatbot on top of a builder
Most tools bolt a chat panel onto a manual editor: the AI suggests, you assemble, and nothing proves the result actually runs. MadMax's builder is agentic end to end:
- Describe: one sentence in the Build with AI box. Plain language, no node names required.
- Plan: Max drafts a step-by-step plan you approve before anything is built. Each row states what the step does and what proves it works.
- Build: the whole graph lands in one pass: triggers, branches, AI steps, integrations, and every edge between them.
- Verify: the part that matters. The server test-runs every step on realistic data: webhooks get sample deliveries, AI steps produce real output, integrations preview the exact request they would send. A step only turns green when its output is judged correct, not merely "it didn't crash."
- Gate on you: anything only a human can settle (connect a Slack account, upload a sample document, approve a risky send) becomes a visible ask on the plan. Nothing silently no-ops.
When every step is verified, the plan shows ready and one click activates the workflow.
What the AI can build with
Everything on the palette is fair game for the AI, the same ~29 step primitives you can drag by hand:
- Triggers: webhooks, schedules, forms, file uploads, inbound email and SMS, watch-style polling that fires once per new item.
- Logic: if/switch branching, loops over lists, merges, reshaping (filter/sort/dedupe/limit), wait steps, sub-workflow calls.
- AI steps: freeform agents with structured output, document extraction with vision, classification, summarization; optional per-agent memory and knowledge-base retrieval (RAG).
- Actions: HTTP requests to any API, integrations like Slack and GitHub, platform email and SMS, and data stores for state between runs.
Editing and asking, not just building
The same chat that builds also edits: on a canvas that already has steps, "also email me a copy" is a targeted change, not a rebuild. Ask is the read-only mode: it explains what a workflow does, why a run failed, or what a step's last output was, which is useful on workflows you didn't build yourself.
Where to go next
- Getting started: ship your first workflow in a few minutes.
- Building workflows: the canvas, steps, and the verify loop in detail.
- Triggers & activation: how workflows go live and fire.