The real expectation shift should be around ownership. Once a SaaS can take action, I no longer want it to just tell me something needs attention, I expect it to either handle it or clearly explain why it stopped.

I had be interested in the rollback side of it. If a natural-language change improves one part of the agent but unexpectedly breaks another, how quickly can you identify and reverse that change?

One thing I will add is auditability. If an agent makes every process consistent, teams should also be able to see exactly what it did, why it did it, and where it deviated, especially when something goes wrong.

AI Follow-Up Agent for Small Businesses

A SaaS that connects to WhatsApp, email and a simple CRM, then actively follows up with leads who showed interest but never completed the next step.

I think I will look at the handoff cost, not just the agent count. Multiple agents can work well, but if users have to repeat context every time ownership changes, the architecture becomes visible to the customer. The best setup may be the one that keeps that boundary invisible.

One thing I’d add is that the first 100 users can be more valuable as a learning network than a growth milestone. Their questions, objections and usage patterns can reveal not only who the product is for, but also what message makes the next 100 easier to acquire.

The real advantage may be less about having one agent everywhere and more about having one source of truth. If a customer switches from WhatsApp to email, the biggest win would be carrying the same context and actions across that journey without making the customer start over.

The hidden cost of AI customisation is often not the initial build – it’s the number of things that can silently break later. I think the key is making customisation modular enough that models or product changes don’t force you to rebuild the whole workflow.

A waitlist can measure interest, but it can’t reveal where the product actually breaks. I’d be more interested in what happens after signup – activation, first successful use, and whether users come back. Those signals tell you far more than the size of the list.

The bigger question for me is how much operational context the AI actually retains. A good AI-native help desk should reduce the number of times agents have to reconstruct a customer’s history before taking action – that’s where I’d expect the biggest day-to-day difference to show.

The interesting metric isn’t how much AI can engage, it’s whether the conversation produces a better-qualified lead. A fast AI interaction that ends with a human handoff could be far more valuable than simply increasing chat volume.

This is the part of PocketBase discussions I find most useful, not “can it handle X requests?” but what starts hurting after the product has real users and real data. Early simplicity can be a huge advantage, but the long-term operational experience is the real test.