Autonomous Product Development Autonomous Product Development<br>written by WILL TAYLORAI, PRODUCT<br>In a routine customer call, a user complained that the text on a button was too technical. Our AI COO had listened to the call and created the task in Linear. Our AI Engineer then shipped the fix.

None of our product team had listened to the call or even been aware of the customer request. A customer simply mentioned it. The app improved. All our other customers benefitted.

This is the Customer-to-Customer Feedback Loop in motion.

At Versey we’ve just started Autonomous Product Development, allowing improvements to be planned and implemented without humans in the loop.

This started as a small open-source side project, but we quickly found that when we needed a little engineering support for routine bugs and maintenance, it was worth deploying into our core app.

The goal is to create a cycle where the interactions with one user ultimately improve the app for everyone - in other words, a Customer-to-Customer Feedback Loop .

Once you achieve a Customer-to-Customer Feedback Loop, your product gets better simply because people are using it in a classic flywheel. It also frees up our team to focus on meaningful projects.

This is a very early “research preview” as the cool kids would call it - just an “n=1” and over a short time horizon. There are potential security and cost footguns as shared below. I hope this inspires you to experiment with an Autonomous Product Development loop in your own companies.

I’ll share:

Our results so far

How this is set up

Mishaps and pitfalls - including spending $2400 overnight the first time we turned it on

What it takes to get here for most other organisations

Customer results

Let’s start by grounding this in how it’s actually helping real customers.

Our startup Versey provides a freemium app for AI-supported writing.

It helps experienced writers finish, on average, around twice as fast. It does this by making editing and getting feedback effortless, bringing the tasks writers usually turn to LLMs for right into the editor.

It’s a real product with real, paying users.

“I’m pretty fast - I can turn out an article in an hour. But now I can do it in half an hour. I don’t give a lot of glowing reviews, but I haven’t been this excited by a technology tool since email.”

Tracey Borrenson, Marketing Advisor and Director at Partnerships & Alliances, and Co-Founder of Ecosystem Research.

“It’s addictive… […] The highlighting, the in-line chat - all pretty extraordinary. It was very helpful for tomorrow’s edition!”

Marcus Lawrence, Editor of Zinstrel, and SIQA Creative Advisory Council member.

A few weeks ago, we realized we might need to hire a new junior engineer to handle a growing number of low-value tasks. We didn’t have the budget. However, recently I had built an open-source side project - an autonomous AI engineer which monitors its environment, creating tasks or receiving them from the team, then building and deploying them to production.

Instead of hiring, we decided to set up the AI Engineer.

Within the first hour of turning it on, it had already shipped around a half dozen fixes and improvements.

A few improvements the engineer added:

If the AI thinks of a suggested rephrasing to go along with any feedback, show the suggestion below the feedback instead of making the user click “preview suggestion”

Give the different chats for a given document names so selecting between them is easier

Add a preview of font sizes on the settings page where this can be controlled

Other tasks included a handful of bug fixes and some genuinely helpful refactors.

But as mentioned the real magic happened when the AI Engineer started pairing up with our AI Chief Operating Officer - an OpenClaw which manages the company’s central data layer - to listen to customer calls and create tickets from them.

Why most customer signal dies

Customer feedback is traditionally passed from the customer success team through to a product owner, who also monitors product analytics, and who may then create tasks for designers and engineers to build.

Having done this the traditional way in startups and larger enterprises, I’d say the fully-loaded cost for even a small improvement is around $500, with a turn-around of days to weeks. With agents that’s down to ~$100, but there is a minimum floor.

Most customer signal goes to waste:

Only a fraction of customer issues are reported or observed

Only a fraction of those get turned into tickets by the Product Owner

Only a fraction of those get prioritised on the development road map - most die in the backlog.

Because time is finite, only a fraction of problems can be solved. Product teams should spend their time “lifting heavy rocks” on projects that will meaningfully improve the company. You shouldn’t hire top engineers and then have them working on trivial optimisations.

With all the human overhead, there’s a long tail of work that never gets...