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Prasenjit Sarkar on X: I keep seeing the same fix across agent stacks this week: teams are ripping the

@stretchcloud@stretchcloudlisted 47m agoreviewed by Jev

I keep seeing the same fix across agent stacks this week: teams are ripping the expensive model out of the middle of their decision loops. TypeSafe shipped Jev in early access on September 15. Not a chat model. They call it a System One model: hand it a state and typed options, it returns calibrated probabilities, no text generation at all. The mechanism is simple. Most agent loops burn a full LLM call on decisions that never needed generation: route to worker A or B, is this result relevant, approve or block this action. TypeSafe claims up to 200x faster inference and 400x lower cost on that class of decision. Ricker's own tests below land at 193x and 444x. What happened next is the real story. Within four days, Cognition's Jared Palmer shipped Kev, a LoRA adapter on Qwen2.5-0.5B trained in 1 hour 45 minutes on a MacBook Pro, with an API close enough to point TypeSafe's own SDK at it. Kev now scales up to a 9B version that trails Jev by about 4.5 points on held out evaluation. Laya-MLX arrived the same week targeting millisecond decisions on Apple Silicon. A community leaderboard, JevBench, already ranks a dozen of these models. The adoption signal convinces me this is not a toy. TanStack AI shipped a native decide() API for typed choices, scores and booleans. Beacon, an open source memory layer, uses Jev to score which coding sessions are worth turning into reusable lessons. Three teams, one primitive, inside a week. RouteLLM out of Berkeley showed back in 2024 that routing simple queries to a cheap model cuts cost over 85% while holding 95% of GPT4 quality, and production semantic routers report 40 to 90% savings today. What changed is that the router stopped being a side project and became a shipped, benchmarked model category with a name. The bottleneck this solves is real: every agent framework has a model sitting in a loop answering questions that never needed a sentence back. My read is that the decision layer becomes as standard a piece of the agent stack as the vector database became for retrieval, and whoever owns the default there owns a lot of the unit economics conversation for the next year of agent infrastructure. https://x.com/0xRicker/status/2101705843200721203 ❤️ 1 likes on X