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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.

  • n8n
  • LangChain
  • Anthropic
  • Veo 3 API
  • Google Sheets
+3 more
01
Business type

AI video production and publishing workflow

02
Core problem

YouTube video production required repeated planning, rendering, stitching, uploading, publishing, and status tracking

03
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.

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.

01

Scheduled end-to-end n8n workflow

02

LangChain story planning agent

03

Anthropic-powered structured story generation

04

Structured Output Parser for predictable schemas

05

Veo 3 render task creation and polling

06

Clip grouping and final video combining

07

YouTube upload and publish workflow

08

Google Sheets run ledger and status updates

09

Explicit wait and polling steps for render reliability

10

Separated upload and publish stages for safer recovery

11

Credential handling through n8n encrypted credentials

12

Operational telemetry for QA and root-cause review

13

Architecture ready for thumbnails, analytics feedback, and Shorts branching

Operating path

How the system flows

Scheduled runs move through story planning, AI video generation, publishing, and telemetry.

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
Outcome

What changed

The system creates a dependable content production pipeline from scheduled trigger to published YouTube video, with every run traceable in Google Sheets. It improves production consistency, reduces repeated manual orchestration, and gives the team a clear operational record of render tasks, publish status, final links, and errors for QA, recovery, and future optimization.

Repeatable video pipeline
Human review loop
Publish status visibility
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