Ship an AI Feature as a Workflow (Not a Prompt)

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Vibe Engineering: Ship an AI-Native CRM + Embedded Agents

Emilio Taylor

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Share this lesson

Go deeper with a course

Vibe Engineering: Ship an AI-Native CRM + Embedded Agents

Emilio Taylor

View syllabus

What you'll learn

Design an AI feature as a workflow (not a prompt)

Turn a messy “AI idea” into clear states, success criteria, and failure/fallback paths.

Add production guardrails: tools, limits, and safe outputs

Define tool boundaries, structured outputs, and execution limits to prevent runaway or unsafe behavior.

Ship a reusable, production-ready AI workflow pattern

Leave with a template you can drop into your own app (React + API + DB) to build reliable AI features.

Get the exact n8n workflow JSON + repo to import and run

Download the JSON + repo and reproduce the demo exactly—import, run, and tweak with minimal setup.

Why this topic matters

Most AI tutorials stop at demos. Real products break without clear states, failure paths, tool permissions, and observability. This lesson shows a workflow-first pattern so you can ship reliable, auditable AI with guardrails—not just prompts. Next: Vibe Engineering (build an AI-native CRM + agents).

You'll learn from

Emilio Taylor

Author of Vibe Engineering | Founder, Visao + Helix | AI Systems for RevOps

I’m Emilio Taylor — founder at Visao and author of Vibe Engineering: The AI Developer’s Guide. I build AI-native products and teach practical “ship-it” patterns that work in real systems (auth, databases, APIs, logging, and cost controls).

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