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ā¢ 1d
š¬š¼ššæ šš š°šµš®ššÆš¼š šŗš¶š“šµš šÆš² š¹šš¶š»š“ šš¼ šš¼ššæ š°šššš¼šŗš²šæš. šš»š± šš¼š š±š¼š»'š š²šš²š» šøš»š¼š š¶š.
Real story from a recent client project.
An ecommerce brand came to me with separate AI agent workflows on n8n, one per platform, handling customer chats on WhatsApp, Facebook, Instagram, and Twitter/X. Sounded fine on paper. Then reality hit: 1,000+ customer messages a day across four channels.
The bot started breaking down:
ā¢ Making up order details that weren't real, straight up lying to customers
ā¢ Pulling the wrong info from their data, wrong context, wrong answers
ā¢ No way to trace why it said what it said
ā¢ Fixing one flow quietly broke another
ā¢ Couldn't handle the volume across four platforms at once
No-code tools like n8n, Zapier, and Make.com are great for simple automation. I use them too.
But a customer-facing AI agent live on four channels isn't simple automation. It's a thinking system under real pressure, and that needs real engineering, not drag-and-drop boxes.
So I rebuilt it as a custom-coded, production-level system:
1. Real RAG retrieval, pulls the correct answer from their data instead of guessing
2. Hybrid search plus reranking, checks data multiple ways and picks the best fit
3. Anti-hallucination guardrails, stops the bot from making things up
4. Structured outputs (Pydantic), clean fixed-format answers, not random text
5. Human-in-the-loop, a real person steps in on anything tricky
6. Full observability, every decision logged, nothing's a black box
7. Automated eval gate, quality check before anything goes live
8. One unified system behind all four platforms, instead of four fragile flows
Result: hallucinations dropped hard, answers got accurate, and for the first time we could explain every single thing the bot said, on every platform.
Simple truth: no-code is great for internal tasks and small automations. But if your AI agent talks to real customers and revenue depends on it, it needs real code, real testing, and real engineering, not just a workflow diagram.
If you're running a similar setup and it's starting to crack under real traffic, happy to talk through what a production-grade version would look like. That's exactly the kind of work I do.
ā¢ 19h
One thing I didn't get into above: the anti-hallucination check isn't a single guardrail, it's a layered check (retrieval confidence + a "do I actually know this" gate before generation). Happy to go deeper on that if anyone's curious.
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a teaser at a fictional AI chatbot. thoughts & feelings? what else would you like to know as a potential, conceptual consumer? hit me with your best shot!
i'm looking to complete this little 'preview' for presentation come friday afternoon.
Cool work!