buschleague

8 ms·

1.
by buschleague 7mo ago
>...if the agent can reason about the gate, it can learn to route around it. This is especially true. Earlier iterations of our build had python backed enforcement modules in an accessible path. The agent would identify the module that w
2.
by buschleague 7mo ago
This is exactly right. The mental model gap is the real risk for AI-first builders. The code works until it doesn't, and when it breaks you have no framework for understanding why. One thing that helped us: externalize the structure th
3.
by buschleague 7mo ago
This isn't a surprise at all. I sat down with the dev team at OpenAI during dev day last year and the biggest shocker to me: these "kids" are over here vibe coding the whole damn thing.
4.
by buschleague 7mo ago
This is exactly why enforcement needs to be architectural. The "challenges around maintainability and scalability" your clients hit exist because their AI workflows had zero structural constraints. The output quality problem isn&#
5.
by buschleague 7mo ago
We run agent teams (Navigator/Driver/Reviewer roles) on a 71K-line codebase. The trust problem is solved by not trusting the agents at all. You enforce externally. Python gates that block task completion until tests pass, acceptan
6.
by buschleague 7mo ago
State management. The agents lose track of what they already did, re-implement things, or contradict decisions from 20 minutes ago. You need external state that survives compaction because the agent can't be trusted to maintain its own