# The Jev decision system: route before the LLM, ~9× smaller context

ChengOS can now put a decision model in front of your agents. Before any large language model runs, **Jev** reads the user's latest request, picks one of the routes you declared on the canvas, and hands the request to a focused agent that carries only the tools it needs. Anything unclear still goes to your general agent, so no capability is lost.

We ran the go/no-go evaluation on 157 English and Chinese requests (routing-eval-v2):

| | Result |
|---|---|
| Routing accuracy | **156 / 157** (99.4%) |
| Context on tool-needing turns | **≈ 9× smaller** (4,277 → 469 tokens) |
| Routing latency | **0.57 s** p50 (chat-model routers: 1.4–2.9 s) |
| End-to-end completion | On par with the general agent, 5–9 above Tool Hub |

A few things worth knowing:

- **The router is an optimization, not a gate.** Chit-chat, ambiguous or multi-intent requests, low confidence, and outages all fall back to the general agent — the worst case is today's behavior.
- **Routing grants no permission.** Every tool call still goes through the deterministic approval policy.
- **Need data to stay on your hosts?** Switch to Laya, which runs on your own hardware and answers in milliseconds, at the cost of falling back more often.
- The residual risk is multi-intent phrasing: every model, Jev included, misrouted the same "email the code review results to the team" request.

📖 Full write-up: [The Jev Decision System](https://chenghub.org/docs/jev-decision-system)
📊 Interactive evaluation report: [Jev semantic routing evaluation](https://claude.ai/artifact/Rp4Hzx2KkkLkgpHHddygg7) (Chinese)
🛠️ Build one: [Semantic Routing with Decision Branch](https://chenghub.org/docs/agent-semantic-routing)

Share your routing scenarios and measurements in the comments.