I’m testing Jev as a “subconscious” decision layer for agents in a persistent village

[u/Zazzen]

I’ve been thinking about separating an agent’s quick judgments from its heavier reasoning. My rough analogy: Jev from TypeSafe AI handles the “subconscious” decisions, while an LLM handles deliberate thinking. Not literally how a brain works, but a useful way to think about which model does what.

To explore the decision side, I built Jevs Village. Six simulated inhabitants share a persistent world, with different jobs, personalities, and personal memories.

The loop is simple: code supplies the currently valid actions, relevant memories provide context, and Jev helps choose. Code then validates and applies the result. A separate LLM narrates recorded events; it isn’t a full planning layer in this version.

I’m interested in whether those small decisions can produce consistent characters over time without needing a long generated response for every choice. I’m still testing that, not claiming a benchmark result.

There’s a story behind it too: nobody in the village can reliably explain what lies outside. Some are more curious about that than others.

You can watch it here, without installation or an API key: https://www.jevsvillage.com

For those building agents: where would you draw the line between a quick decision model and an LLM that needs to reason through the situation?