Cedric Builds

Strict prompts help a lot in the beginning, but I’ve already seen them drift too. Validating the response shape before booking makes total sense that way a format change fails explicitly instead of creating fake slots. How are you doing the validation in n8n? Simple JSON schema check or something more robust?

@ceeddy492 strict prompts are the thing that drifts. what survives is validating the calendar response shape before you book, so a format change fails loud instead of hallucinating a slot. written with ai

This week I finally shipped my first real 24/7 AI booking agent in n8n. It checks Google Calendar live, talks to customers, and books appointments completely on autopilot. But building it was way messier than I expected.

word.......100% agree Matt. Shipping it felt good, but I’m already thinking about week 6 Right now I’m fighting prompt drift and calendar format changes. Added very strict system prompts + tool grounding to keep it stable.Have you found any good patterns that survive after the honeymoon phase? Also saw the Runner post – looks clean

@ceeddy492 shipping it is the easy week. the scoreboard for a booking agent is week 6, does it still book clean when google calendar shifts a response format, or does the hallucinated-slot problem creep back the moment the system prompt drifts. runner.now/?utm_source=s4… written with ai

Haha exactly ......herding digital cats is the perfect description The trickiest part by far was the Google Calendar tool + hallucinations. Rewrote the tool 4 times because of weird API responses and time zone issues (Kenya +254). Then the real battle was making the AI stop inventing fake slots. What’s been your hardest part when building agents?

@ceeddy492 That's the spirit! Automating booking is a game-changer. The "messier than expected" part is so real with AI projects though – like herding digital cats. What was the trickiest part of the n8n build?

Spot on fr In testing everything looked perfect. The moment real customers started chatting, the hallucinations and edge cases came out of nowhere. That’s exactly why I ended up adding very strict system prompts + heavy tool grounding. How do you guys usually handle post-launch reliability on the tools you build for clients? Any go-to patterns? Appreciate the insight though!

@ceeddy492 One of the biggest surprises is that reliability problems often appear after launch, not before. A workflow can work perfectly in testing and still fail when real users introduce edge cases nobody planned for. That's where most adoption problems start. aiforbusinessautomation.com/ai-adoption-wh…

Biggest lessons from the week: Google Calendar API is sneaky as hell (time zone issues + weird formats). Rewrote the tool 4 times. Hallucinations were the biggest enemy the agent kept confidently inventing fake slots and customer names A very strict system prompt is the best weapon.

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Was watching a video on YouTube there's a lake somewhere in the world where there's full time lightning.. 24hrs and it's pretty interesting

Thanks man! Interesting to hear you guys are seeing similar patterns at Cognitix. How do you guys handle the hallucination battle on the calendar/booking side? Do you also use very strict system prompts or do you have other tricks? Would love to learn how you solve it on bigger agency projects.

@ceeddy492 Great point! We're seeing similar architecture patterns at Coginitix when we build for US agencies

Behind the scenes of the 24/7 AI booking agent I shipped yesterday Everyone sees the clean final workflow… but building it was messy. The hardest part wasn’t just making it work it was fighting the AI’s hallucinations the whole time.

Exactly this 😂 The hardest part isn’t making the agent book appointments, it’s stopping it from confidently inventing fake slots, making up customer names, or confirming things that never happened. My fix was adding a very strict system prompt that says: - Only use data from the calendar tool - If no slots available → say “No slots available” - Never invent information Still not 100% bulletproof, but it cut hallucinations dramatically. How are you handling it on your end?

@ceeddy492 The hard part with AI agents isn't making them do things, it's making them stop doing things they shouldn't. How do you handle that?

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Moral of the story: Building AI agents isn’t just connecting nodes. Half the work is taming the model so it doesn’t lie to your customers 😂Still early with Zana AI, but these painful lessons are making the tools actually reliable.What’s the wildest hallucination you’ve fought while building agents?#BuildingInPublic #n8n #ZanaAI

Then came the real killer ........hallucinations.Even with Google Gemini, the agent kept: Inventing fake available slots Making up customer names or confirmation details Confidently giving wrong answers I went deep into the docs and learned the hard way: You have to explicitly tell the AI “If you’re not 100% sure, say ‘I don’t know’” Ground it with direct tool output instead of letting it “think” Use memory + structured prompts so it doesn’t freestyle

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