Warp

Try Warp for free today → https://oz.dev/ai-labsyt AI agent workflows replace the old design-to-code handoff. We break down the full build pipeline: PRD, prototype, and backend — following principles that apply across claude code, codex, cursor ai, and antigravity tools. Link to the Frontend Design Skill: https://claude.com/blog/improving-frontend-design-through-skills What AI Agents Replaced in the Build Process Jenny Wen (head of design at Claude, formerly design director at Figma) laid this out on Lenny's Podcast: the old design pipeline existed to protect against expensive mistakes. AI made those mistakes cheap. The ritual collapsed. We walk through exactly what changed and the step-by-step process that replaced it. Why the Old Process Existed - Requirements, Figma mockup, frontend handoff, parallel backend: every step protected against building the wrong thing at scale - Simon Willison's key insight: AI significantly reduces the cost of building the wrong thing, so the protection became unnecessary overhead - Engineering speed changed first — Jenny went from 60-70% of her time prototyping to 30-40% - Vision timelines dropped from 2-5 years to 3-6 months The Replacement Pipeline (Stage by Stage) - Start with actors, not features — who uses the system determines what it needs - For each actor: how they get in, what they see on arrival, what they're trying to do, what they cannot do - PRD prompt: interview-style questions that produce a structured requirements file - Layer prompt: converts PRD into pages, modals, and user flows (architecture.md) - Frontend prototype in Next.js using a general-purpose frontend skill — mock data only, no backend yet - Show the client something real and get approval before writing a line of backend code - Task list generated from architecture.md keeps the agent on track - API spec: agent reads frontend code, PRD, and architecture file to write it - Database schema: generated from the same three documents - Supabase MCP: automates project creation, schema queries, and migrations — no manual SQL - Backend layer added last: payments, notifications, rate limiting, analytics How to Build an AI Agent Prototype — Who This Applies To This functions as a complete ai agent tutorial and ai agent course for teams moving off the old Figma-handoff model. If you're figuring out what is an ai agent build pipeline, how to build an ai agent from scratch, or how to build ai agent workflows into a client process, this is the current answer. The actor-first logic applies directly to n8n ai agent pipelines and n8n ai agent tutorial setups — define who interacts with the system before defining what to build. Builders using claude ai agent environments will recognize the architecture.md pattern from claude code workflows. The same sequencing applies whether you're running chatgpt, google ai studio, or gemini as your underlying model. For ai voice agent and manus ai agent setups, the same principle holds: requirements and structure before execution. Tool-Agnostic Applications - vibe coding builders: same process, less ceremony — prototype first, connect services last - claude cowork teams: the PRD file and architecture.md replace the old Figma handoff entirely - These workflows reflect where ai news around agentic builds is pointing — the design-to-code handoff is collapsing industry-wide - The prompts shown on screen are available as a step-by-step workflow in AI Labs Pro Hashtags: #ai #ClaudeCode #n8n #claudeCowork #claude #Gemini #vibeCoding #ChatGPT #GoogleAIStudio #aiNews