I’m exploring advanced agent-based architectures in n8n where a primary AI agent can design, configure, and deploy secondary agents autonomously based on task requirements. Specifically, I’m trying to understand: - How to architect an agent supervisor / orchestrator pattern in n8n - Whether people are using meta-agents that generate: - Best practices for doing this with: - Guardrails for: I’m not looking for “auto-GPT style demos,” but production-grade patterns that work reliably inside n8n. If anyone has: - architecture diagrams - workflow patterns - real-world examples - or lessons learned from failed attempts I’d really appreciate insights.

University admissions. 1,200 transcripts annually. Manual entry consuming 900 hours. Built n8n workflow. Zero manual transcription. Perfect accuracy. THE ADMISSIONS PROBLEM: Every incoming transcript requiring manual processing. Coordinator downloads PDF from email attachment. Opens admissions system. Types student details into database. Enters institution information manually. Transcribes all courses individually - course codes, course names, credit hours, letter grades, semester completed. 15-30 courses per transcript average. Each course typed manually. Manual GPA verification against transcript calculation. Academic honors notation if present. Verification status determination based on transcript type. 45 minutes per transcript. 1,200 transcripts annually. 900 hours consumed. Quality audit revealed 14.8% error rate. Course entry typos. GPA calculation mistakes. Overlooked academic honors. Inconsistent verification flagging across coordinators. THE n8n AUTOMATION: 7-node workflow with 3-branch verification routing: Node 1 - Gmail Trigger: Monitors transcript emails Node 2 - Get Email: Downloads attachment Node 3 - Prepare Binary: Formats document Node 4 - Extract Data: Pulls student info, courses, grades, GPA, honors Node 5 - Analyze Transcript: Calculates metrics, categorizes performance Node 6 - Log Database: Updates tracking Node 7-9 - Verification Routing: Routes to verification check OR unverified path EXTRACTION: Student - name, ID, DOB, email Institution - name, address, registrar Degree - program, major, minor, dates Courses - code, name, credits, grade, semester, year (all courses) Academic - credits attempted/earned, GPA, honors Metadata - type (official/unofficial), date, signature ANALYSIS: Total courses count Completion rate percentage GPA categorization (Exceptional/Excellent/Good/Fair) Academic standing (Dean's List/Good Standing/Probation) Grade distribution summary Verification evaluation (official + student ID) VERIFICATION LOGIC:

In this video, I walk through how to actually deliver AI projects, covering APIs, hosting, and handover so clients can use what you build without things breaking or getting messy. This is for anyone building or selling AI services who wants clean delivery, happy clients, and fewer problems after launch. Hope you guys enjoy!

This is a great time to reflect on the past year, set a few intentions for the year ahead, and most importantly rest, recharge, and spend time with family. I know I will be. The AI space will still be here when we get back! Cheers, Nate

Let's get to know each other! Comment below sharing where you are in the world, a career goal you have, and something you like to do for fun. 😊