The distinction between a procedural graph and a static workflow seems important: self-editing topology can capture reusable strategy, but it also makes regressions harder to localize. I’d be curious whether the refinement loop treats a successful trajectory as sufficient evidence, or uses counterexamples and held-out tasks to avoid encoding a brittle shortcut. A practical evaluation might report graph churn and rollback frequency alongside task success, since a graph that keeps growing could be trading inference cost and auditability for a small gain. The explicit entity–relation–procedure representation also seems like a promising place to attach permissions or provenance to tool calls.

What if open-ended agents are overkill for 99% of problems? Let's just take that premise for a second. Most organizations want to follow "best practices" and train their employees to do so. Hiring a genius and giving him total freedom to complete every task is not what companies usually want for MOST things. They want repeatability, reliability, predictability. Especially if they are regulated.

I'm going to drop a bomb over here: what if agents are the root of all our problems in AI safety, cost, and even adoption by regulated organizations? I really do believe this. Here is what I argue:

https://safebots.ai/agents.html

https://safebots.ai/kimi.html

And here is my overall thesis:

https://safebots.ai/thesis.html

If you do manage to read (or skim) that, I welcome any questions, comments or rebuttals.