When we launched our software year we set the price at nine dollars a month because we were afraid that nobody would pay more for an early stage tool. Signups came in fast. Our support costs soon grew faster, than our revenue and working late nights became unsustainable.

Of raising the price on the entire public landing page we ran a quiet experiment with new visitors. We added a forty nine dollar tier that included priority setup support. We expected the conversion rate to fall to zero. Surprisingly twenty percent of new users chose the higher tier.

The higher price attracted serious users who needed less hand holding and who valued the software s core utility. Our monthly recurring revenue tripled, even though the customer volume was cut in half proving that underpricing was our bottleneck.

How did you find the courage to raise your price. How did your early users react?

I've been following the AI agent launches here, and one question keeps coming to mind.

We're building agents that write code, take meeting notes, conduct research, and execute workflows. In manufacturing, robots are already performing physical work.

But are we rethinking how organizations operate, or simply teaching AI to perform the same processes faster?

Each strategic initiative has its own goals and KPIs, but the data is spread across different teams and tools.

Before every leadership review, someone has to chase updates by email, wait for responses, reconcile numbers, and piece everything together.

The leaders I interviewed described reporting cycles that consumed 20 60 hours of manual effort. And by the time the information reached leadership, some of it was already outdated.

But what interested me most wasn't just the time being lost.

When a user is flagged by my safety layer, the app goes quiet for a while. I kept the whole rule in one pure function: it takes the user s recent flags and the current time, and returns whether they re paused, until when, and why. No database, no clock reads inside, and the lengths live in named constants (pause length, repeat window, minimum gap between two flags that count as a repeat).

The rule itself is small, but time rules go wrong at the edges: exactly at a boundary, two flags a minute apart, a timestamp with microseconds and a +00:00 offset. So I wrote 61 tests, then did something I now do for every rule like this: I broke the code on purpose, seven different ways, and checked that a test failed each time. I removed the minimum gap, counted flags I shouldn t count, measured a repeat from the wrong flag, made the pause read fail closed, and so on. All seven were caught. A test that has never failed proves nothing.

Two things the design review found before users did:

1. I first started the soft period (gentler messaging after a pause) at the flag time. That meant it ended the moment the longest pause ended, which is the worst time to switch back to normal. I moved the start to the pause end. That quietly broke something else: the database query only looked back as far as the old rule needed, so a repeat flag would have fallen outside it while the soft period was still running. The look-back had to grow to cover pause plus soft period plus repeat window. A test now fails if I shrink it.

2. Contact-form flags must not count as repeats. The table holds only a user ID, a level, and a time, and I didn t want a text column, so I added a single yes/no column instead.

We recently checked our helpdesk logs and found that a single free user opened over 30 tickets in under a month asking for custom walkthroughs and direct setup help. After our engineers spent hours fixing their workflows, they straight up refused to upgrade to a $20/mo plan because they got everything for free. It made us realize we were running an unpaid agency. If you run a SaaS, do you cut off manual support for free users entirely, or just route them to slow email queues and docs?

I always thought that the common answer to agent permissions is to let the agent act on anything you can undo and request only for crucial part. Sending a customer a messages usually would count as the thing you can't take back I guess.

I'm not sure that line holds for product decisions. In a past project, an agent just changed something that was easy to revert. But if we hadn't noticed, it could have set off a chain of follow-up actions that caused us big trouble under their own understanding. Another instance, when the AI agent marks a customer request as "planned." instead of Notice. It would be great if we found the issue early on, but if not, we might have missed this request

Meanwhile asking for approval with every changes would make things more secure, but when there are too many requests, I notice that people start approving without reading.

For people using agents around product decisions:

There is a massive trap in expecting an AI to reliably output matching JSON schemas or proper frontmatter without breaking formatting under the hood.

We finally cut out model generated paths and dates entirely, handing those chores over to a simple local hook that feeds exact parameters before every request. The model only focuses on narrative text now, and validation errors dropped to zero.

How do you handle strict data formatting in your agent workflows without constantly dealing with broken syntax?

When a customer who pays your bills asks for a custom feature, saying no feels almost impossible. But we learned the hard way that building every single request turned our app into a bloated mess of half-baked tools. Most requests are just users trying to patch over a workflow problem they don't know how to explain yet. How do you tell high-value clients that their requested feature isn't on the roadmap without completely ruining the relationship?