Business type
AI video production and publishing workflow
All case studies
AI Video · n8n Automation · YouTube
AI-Generated YouTube Production Pipeline
An automated AI-assisted YouTube production system built in n8n, designed to run on a schedule, generate structured story plans, request AI video renders, combine clips into final videos, publish to YouTube, and log every run in Google Sheets for visibility.
Core problem
YouTube video production required repeated planning, rendering, stitching, uploading, publishing, and status tracking
Main outcome
A production-grade plan-to-publish automation pipeline with observability, recoverable publishing states, and consistent content operations
Business impact
Turned a manual video-production process into a repeatable automation pipeline, reducing coordination work across planning, generation, publishing, and tracking.
Repeatable video pipeline
Human review loop
Publish status visibility
The challenge
The business friction
AI video production can still become operationally messy when every run depends on manual planning, prompt preparation, render requests, polling, clip ordering, final video assembly, upload, publish, and status tracking. The client needed a dependable automation system that could support a predictable publishing cadence, expose every operational state, and make failures recoverable without relying on guesswork.
The solution
The system response
MASK AI built a production-ready n8n automation that starts from a schedule trigger and routes the work through a LangChain story agent powered by Anthropic. The agent produces structured story plans using a deterministic output parser, then code nodes normalize the plan into prompts and clip specs. HTTP nodes create Veo 3 render tasks, wait and poll for completion, group clips into narrative order, request a combined final video, upload it to YouTube, publish it as configured, and update Google Sheets with task IDs, URLs, timestamps, publish status, and error information. The system separates upload from publish, uses explicit wait states for render reliability, and keeps credentials inside n8n's encrypted credential store.
Capability map
What the system covers
Built around the operational jobs that matter: intake, product experience, workflow control, visibility, and follow-through.
Scheduled end-to-end n8n workflow
LangChain story planning agent
Anthropic-powered structured story generation
Structured Output Parser for predictable schemas
Veo 3 render task creation and polling
Clip grouping and final video combining
YouTube upload and publish workflow
Google Sheets run ledger and status updates
Explicit wait and polling steps for render reliability
Separated upload and publish stages for safer recovery
Credential handling through n8n encrypted credentials
Operational telemetry for QA and root-cause review
Architecture ready for thumbnails, analytics feedback, and Shorts branching
Operating path
How the system flows
Plan-to-publish automation
Scheduled runs move through story planning, AI video generation, publishing, and telemetry.
08 stages
01
Schedule Trigger
02
Story Agent
03
Create Veo 3 Render
04
Poll Clip Status
05
Group and Combine Clips
06
Fetch Final Video
07
Upload to YouTube
08
Publish and Log Status
Technology
Stack and architecture
The engagement combined product interface work, workflow logic, and implementation tooling around the operational needs of the project.
- n8n
- LangChain
- Anthropic
- Veo 3 API
- Google Sheets
- YouTube API
- HTTP Nodes
- Structured Output Parser
