Tags: MULTI-AGENT AI SYSTEM · SUPPLY CHAIN INTELLIGENCE · LANGGRAPH + GROQ + FASTAPI
One Line: Replacing the reactive, spreadsheet-driven supply chain monitoring cycle with an autonomous multi-agent pipeline that monitors suppliers in real time, traces cascade risk through inventory and orders, calculates financial exposure, and drafts communications — before a single production halt occurs.
| # | Section | What It Covers |
|---|---|---|
| 01 | Problem Framing | The supply chain blindspot in SMB manufacturing and why current tools fail |
| 02 | Solution Overview | Four-agent pipeline and how each agent works |
| 03 | Multi-Agent AI Architecture | Why LangGraph, Groq, and the agent separation model were chosen |
| 04 | Agent 1 — Supplier Intelligence | Real-time web search, LLM reasoning, alert creation |
| 05 | Agent 2 — Order Risk | BOM traversal, inventory cross-reference, cascade tracing |
| 06 | Agent 3 — Financial Impact | Revenue exposure calculation, risk assessment generation |
| 07 | Agent 4 — Response Agent | AI-drafted supplier emails and customer notices, approval queue |
| 08 | The Dashboard — Eight Screens | Every screen in detail including role-based access |
| 09 | Technical Architecture — Key Design Decisions | Every architectural decision with rationale |
| 10 | Database Schema — 15 Tables | Full Supabase schema, RLS policies, seed data |
| 11 | The First Live Agent Run — NovaBrew | Proof the system works, real output, cascade detection |
| 12 | Live Links & Resources | Public URLs and access points |
| 13 | Metrics Framework | Agent performance, pipeline health, business impact |
| 14 | Build Journey & Decision Log | Why each tool was chosen, what failed, what was learned |
| 15 | Product Roadmap | 5-phase plan from demo to enterprise platform |
| 4 Autonomous Agents | 15 Database Tables | 0 Pre-written Rules |
|---|---|---|
| Supplier Intelligence → Order Risk → Financial Impact → Response | Full supply chain data model from PO to customer order | Every risk finding is reasoned from live web data, not hardcoded logic |
| $190K Revenue at Risk Detected | 13 API Endpoints | Fully Deployed |
| --- | --- | --- |
| Live NovaBrew demo pipeline identified $190,117 at risk in first run | FastAPI backend on Oracle Cloud via Cloudflare Tunnel | Frontend on Vercel, backend on Oracle Cloud, database on Supabase |
03 · Multi-Agent AI Architecture
04 · Agent 1 — Supplier Intelligence
06 · Agent 3 — Financial Impact
08 · The Dashboard — Eight Screens
09 · Technical Architecture — Key Design Decisions
10 · Database Schema — 15 Tables