Hi everyone, I am building a production-ready SaaS for vehicle damage appraisals (B2B). The system needs to be "bulletproof" as I plan to scale this to multiple clients. The Workflow is complex: 1. Data Collection: gathering 80+ fields (dynamic logic based on private/company, owner/driver status). 2. Document Handling: Requesting specific docs, OCR analysis, and validation. 3. Support: Answering FAQs from a Knowledge Base. 4. Scheduling: Booking appointments. I am currently debating the smartest architecture to ensure reliability and context retention. The Options I'm considering: - Option A: The "God Agent" (Monolith) One single Agent (GPT-4o) with a massive system prompt handling everything. - Option B: Router + Specialists (The Swarm) A fast/cheap Prequalifier/Router (e.g., gpt-4o-mini) classifying the intent, then routing to: My Question: For a high-stakes SaaS where data integrity is key: Is the complexity of orchestrating multiple agents worth it? Or is a single, well-prompted GPT-4o robust enough to handle context switching without breaking the flow? Would love to hear your experiences with production apps! Thanks! 🚀

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I'm building an automotive damage assessment chatbot workflow in n8n that collects customer information through conversation. I'm facing a challenge with data extraction and storage strategy. ## Current Setup - **n8n workflow** with AI Agent (Google Gemini) - **PostgreSQL database** (`claim_sessions` table with ~50 columns) - **Two data sources:** 1. **OCR extraction** from uploaded documents (ID, vehicle registration, police reports) - field names are known and consistent 2. **Chat conversations** where AI collects data like name, address, accident details, etc. - field names are unpredictable ## The Problem For OCR data, I can easily normalize field names since I control the OCR prompt. But for chat-collected data, the AI agent might name fields arbitrarily: - User: "My name is Max Mustermann" - AI might store as: `{name: "Max Mustermann"}` or `{vorname: "Max", nachname: "Mustermann"}` or `{customer_name: "..."}` I need consistent field names matching my database schema (e.g., `vorname`, `nachname`, `strasse_nr`, `plz`, `kennzeichen`, etc.) ## Options I'm Considering ### Option 1: Structured Output Parser - Add Output Parser node to AI Agent - Force AI to output JSON with exact schema every response - **Pros:** Guaranteed structure, direct DB insert - **Cons:** Verbose (AI outputs full schema every message), complex setup, might limit natural conversation ### Option 2: Post-Processing Data Extraction - AI Agent generates natural conversation - After each response, run separate AI call to extract structured data from the conversation - Use Claude/Gemini API with prompt: "Extract all mentioned data into this exact JSON schema: {vorname, nachname, ...}" - **Pros:** Natural conversation, flexible - **Cons:** Extra API call per message, costs more tokens, slight latency ### Option 3: Hybrid Approach - OCR data → Automatic normalization & DB save (works great) - Chat data → Keep in Chat Memory only - Final step → AI summarizes all collected info, user confirms, then save to DB

If you’re building RAG agents in n8n, this is one of the most important tutorials you’ll ever watch. In this step-by-step video, I’ll show you how to build a RAG (Retrieval-Augmented Generation) pipeline completely with no code. This setup automatically keeps your database synced with your source files, so when you update or delete a file, your database updates too. That means your AI agents always search through accurate, trustworthy data instead of outdated information. Without this system in place, you can’t rely on your AI’s answers at all. By the end of this video, you’ll understand exactly how to connect everything inside n8n, Google Drive, and Supabase, even if you’re a complete beginner.