I’m not a Zapier expert but I know they have thousands of app connections and some kind of automated agents feature. My business partner and I are thinking about making a platform where people can create AI assistants using simple prompts. The idea is to connect these assistants to different apps and make them learn from mistakes over time.
But now I’m wondering if this makes sense. Most business automation needs seem like they could be solved with existing tools. I can only think of a few consumer scenarios like booking medical appointments or finding restaurants where a custom AI agent would actually be worth it.
If anyone knows more about what Zapier can and cannot do, I’d love to hear your thoughts. Are we trying to solve a problem that’s already been solved? What kind of automation tasks would actually need something more advanced than what’s already out there?
Zapier works great for simple triggers, but hits a wall fast when you need real intelligence or complex decisions.
I see this all the time at work. Zapier moves data when X happens, but can’t understand context, handle exceptions, or adapt. It’s like having a copy-paste robot instead of a thinking assistant.
Here’s where it breaks: What if your automation needs to read an email, understand the intent, check multiple systems, make a judgment call, then take different actions? Zapier chokes on anything beyond basic if-then logic.
Your AI assistant idea fixes real problems. The learning from mistakes part is especially valuable - most platforms are static and do the same thing forever until someone manually updates them.
Don’t build from scratch though. Prototype way faster with something like Latenode. It handles app connections and workflow logic, but lets you inject AI decisions and custom logic wherever needed. You can iterate on AI components without rebuilding all the integration work.
Your restaurant booking example is perfect for this. You need something that understands preferences, handles edge cases, and gets smarter over time.
Zapier’s biggest problem? It’s just rule-based. It follows workflows without actually understanding what it’s doing. I’ve used automation tools for three years, and this gap hits you hard when you need real decision-making. I tried using Zapier to route support tickets - it could sort by keywords but missed all the nuance and urgency a human would catch instantly. Your AI assistant idea is completely different. Zapier just connects apps and shuffles data around. You’re building something that can actually think, reason, and get better over time. That’s huge for handling messy situations where automation needs to make judgment calls. Your medical booking example nails it. Sure, current tools can sync calendars, but they can’t handle conversations, figure out what patients actually prefer, or adapt to different clinic setups on the fly. There’s real market opportunity here, especially if you target problems that need genuine understanding instead of basic data movement. The learning aspect alone puts it in a different league from static platforms.
zapier’s like connecting legos - works great until you need something custom. i’ve run into this with client onboarding automation where you actually need conversational ai, not just moving data around. your ai approach sounds way better for handling those weird edge cases that break standard workflows.
I’ve worked with custom automation and Zapier, and here’s the key difference: adaptability and context awareness. Zapier’s great for linear workflows but falls apart when you need dynamic responses or have to deal with incomplete info. I’ve seen this constantly in customer service - Zapier can route tickets based on set criteria, but it can’t read tone, gauge urgency, or handle customers who give vague problem descriptions. Your AI assistant concept fixes this with real comprehension and learning. The sweet spot is where automation needs to fill gaps or handle messy inputs. Like expense reports with terrible scanned receipts, or inventory management where suppliers can’t agree on naming conventions. Current platforms need perfect, structured data or they break. What’s really compelling about your approach is the learning piece. Traditional platforms stay frozen until someone manually updates them, but an AI system that learns from experience could tackle complex edge cases without constant babysitting. This is huge in industries with frequent regulatory changes or evolving processes where rigid automation becomes useless fast.