Build Your First AI Workflow with AI Workflow Builder: Complete Tutorial

Build Your First AI Workflow with AI Workflow Builder: Complete Tutorial

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This tutorial takes you through AI Workflow Builder from installation all the way to “first workflow live.” In about 30 minutes, you will build a real, working multi-agent workflow: daily competitor price monitoring.

Prerequisites

• Node.js 22.5+ (uses built-in node:sqlite)
• npm 10+
• A GitHub account (for publishing step, optional)

Step 0: Installation

git clone https://github.com/slashman413/ai-workflow-builder.git
cd ai-workflow-builder
npm install          # install server + web workspaces
npm run dev          # server :4000 + studio :5173

Open http://localhost:5173. Development mode provides simulated login (Continue with GitHub), no real credentials needed.

Step 1: Enter Your First Prompt

Type the following into the prompt input in Studio:

Build a market research agent that scrapes competitor pricing daily,
analyzes trends, and emails a morning summary to the team

Click Grill me →. AI Workflow Builder does not start building immediately — it launches the Grill-Me Q&A loop first, asking only about the genuinely ambiguous parts of your prompt.

Step 2: Answer Grill-Me Questions (~3-5 Questions)

You will be asked questions like the following:

Q: What single concrete deliverable must a successful execution produce? → A daily markdown report with prices for all 10 competitors

Q: Where does the competitor list come from? → A URL list I provide in the project settings

Q: How do you know it ran correctly this time? → All 10 competitors have prices, and sampled values match the source pages

Q: What happens when a site blocks scraping? → Flag it as needing human review and continue with the rest

After each answer, the coverage indicator on the right updates. When all critical dimensions show green (ready: true), you can proceed to the next step.

Step 3: Generate and Validate the Workflow DAG

Click Scaffold. The system will:

1. Assemble answers into a versioned specification (spec.yaml)
2. Build the workflow DAG — roughly like this:

[URL List] → [Scraper × 10] → [Parser] → [Compare/Verify] → [Summary Gen] → [Email Send]
                                        ↑                    ↑
                                  [Change Detector]    [Human Review Queue]

3. Run pre-flight static validation: cycles, reachability, schema matching, tool boundaries, safety boundaries

If the DAG has structural issues (e.g., the parser is skipped and raw HTML is fed straight to the summary node), pre-flight rejects it immediately and tells you why — before executing anything.

Step 4: Review the Generated Python Code

The scaffolded project includes:

interfaces.py          # Typed interfaces, each node has explicit input/output types
main.py                # Executable entrypoint, resilient main loop with continue_on_error=True
workflow.json          # Validated DAG definition
spec.yaml              # Versioned spec (source of truth)
.github/workflows/ci.yml  # CI from day one

LLM calls come standard with retry + fallback (LLM_MAX_RETRIES, DEFAULT_AGENT_FALLBACK) — LLM calls are unreliable in production, and that is a standard feature, not an option.

Step 5: One-Click Publish to GitHub (Optional)

Click Publish, authorize with GitHub OAuth (repo scope), and the compiled workflow scaffolds into a brand-new repository within seconds. CI starts running immediately. Every publish is logged in the publications ledger — the publish history is auditable.

Step 6: Hand Off to a Scheduler

The published workflow is a standard Python project:

pip install -r requirements.txt
# Run daily via cron or any scheduler
0 9 * * * cd /path/to/workflow && python main.py

Frequently Asked Questions

Q: What can I run on the free tier? A: 10 Grill sessions per month + mock preview. Team tier ($99/mo, 14-day trial) unlocks unlimited Grill and GitHub publishing. Perpetual source-code license is fully unrestricted — you deploy it yourself.

Q: Does the service execute my workflow? A: No. Studio only handles validation and mocking (mock); actual execution happens on your local/self-hosted generated code.

Q: Where do I store my LLM key? A: The built-in vault stores it using envelope encryption (AES-256-GCM) — it is never displayed in plaintext.

Q: Does it conflict with Cowork Pro? A: No. Builder designs and validates workflows; Cowork Pro dispatches execution tasks long-term. Recommended workflow: Builder designs → Cowork executes.

Next Steps

• Read How to Design Multi-Agent AI Workflows — understand the methodology
• Read Grill-Me Interaction Specification Design — understand the Q&A engine
• Purchase AI Workflow Builder ($99) and start building your own multi-agent systems