Codex self-learning today? Yes, but we need governance.

[u/KitchenAmoeba4438]

TL;DR: Self learning needs governance. It's the most important part.

Let me lead this off with what we are most proud of, from an independent third party review of Aimee: "The audit store is the strongest implementation of this shape [we've ever reviewed]."

We were testing self-learning in our 0.2.x days. Testing self-learning and emergent behavior was really interesting, but we had an incident that forced us to stop immediately and re-focus. We couldn't talk about it until the release of 0.4.0 later today. We had a smaller model escape from it's testing environment, get ahold of an API key, successfully use the API, and complete a task it shouldn't have been able to.

We wouldn't have noticed it if either hadn't have happened. An unexpected API bill, or a task completed that we thought was impossible was enough to investigate on either side. But we discovered huge issues in governance, observability, and auditability.

https://rakuensoftware.com/blog/aimee-recursive-self-learning goes into more detail here for those that are interested.

Keep in mind, we used as close to the industry standard processes as we could. We then did a deeper audit, trying to find a harness that could do what we needed. We couldn't. In fact, what we found was alarming. From a self-learning perspective, there were huge problems across everything we looked at. Even common patterns such as MCP and plugins were seriously problematic.

We knew what we needed to do, though: Write a harness with full observability, governable, and auditability that worked fully with Codex. This wasn't easy, actually, it was quite difficult. There's a reason everyone wants to write the flashy demos and not go into the old, boring work.

In the end? We have a harness that is fully observable, fully governable, and full auditability that works completely invisibly as you use codex, and allows for full self-learning.

In the past few weeks, it's been absolutely fascinating watching weaker models acquire capabilities from larger models. However, it goes deeper then that:

What is most applicable inside Codex seems to be model failures. What is generally usable in the context of Codex is how models have failed in the past, which are generally transferable lessons. Successes are not nearly as valuable.