Chase on X: Jev Engineers just released Jev, and the architecture is simple: state goes in, Jev Engineers just released Jev, and the architecture is simple: state goes in, auditable decisions come out
I mapped the decision layer into a field blueprint: what Jev reads, what it returns, and how an agent uses those answers in a real loop:
step 1 → give each layer a job. The LLM plans and writes. Jev chooses between bounded options. Code carries out the action.
step 2 → build the state before asking for a decision. Include the goal, evidence, available actions, previous actions, and what the environment looks like now.
step 3 → define the questions in code. Choice selects an option. Score rates it. Noul estimates whether a condition is true. The answers are typed, so the runtime can use them directly.
step 4 → ask independent questions together. One state snapshot can support routing, risk, and relevance decisions in the same pass.
step 5 → use the answers to route work. A lookup, a complex reasoning task, and a tool call don’t need the same model or execution path.
step 6 → add a gate before the tool runs: allow, ask, or deny. A high-confidence answer still has to pass the action policy.
step 7 → execute the selected action and check the outcome. Record what the tool actually did, including failures.
step 8 → update the state with that result. Otherwise, the agent’s next decision is based on a world that no longer exists.
step 9 → apply the pattern where small decisions repeat: model routing, tool guardrails, inbox triage, research classification, browser actions, and context compaction.
step 10 → judge the whole task. Track whether the agent reached its goal alongside cost, latency, approvals, and failed actions.
the result: you can trace an action from the evidence Jev received to the decision it returned and the outcome your tools produced.
Save the Jev Decision Infrastructure blueprint for your next agent build ↓
❤️ 16 likes on X
@0xChaseTM
