Adding a Jev decision layer to agents: act or escalate by confidence
The muse-jev-playbook inserts a Jev decision layer before expensive steps like browser use and deep research, using 0.8 and 0.5 thresholds to decide whether to act, hand off to a human, or escalate.
0.67 confidence. Your agent acted on it anyway. 99% of agent setups never set a bar. The 1% know 0.67 is the do-not-act band. A decision layer sits in front of the expensive steps - browser, deep research, retries, subagents - and decides whether they happen at all. setup is a clone and a config : state -> one Jev call -> policy -> act, surface, or escalate step 1 -> export your TypeSafe key in your shell profile, not in the session step 2 -> clone muse-jev-playbook, venv, requirements, copy config.example.yaml step 3 -> build a compact state before every fork: goal, kind, cache hints, error history, constraints step 4 -> ask all three primitives in ONE parallel call: choice, score, noul, each with a confidence 0-1 step 5 -> branch on policy, never on vibes: 0.80 and up act, 0.50 to 0.79 go to a human, below 0.50 escalate step 6 -> run it in shadow, read the log, flip to active only when the log agrees with you step 7 -> use the recipes instead of writing prompts: triage, rank options, act-or-wait, retry gate, research cap, approval gate the result, from the playbook's own flight case: - 92 days of fares collected, range $698 to $895, median $851 - narrowed to 8 October days before any deep dive - $153 under the median, 18% the part the setup posts leave out: that same case asked for the single best day and got 0.67. surface band, not act band. on the round trip, book-now came back 0.79, one hundredth under the bar. so the layer hands you the month at 1.0 and gives the day back to a human. that is the policy working, not failing. a decision layer does not make an agent smarter. it makes it STOP in the right places. on a real bill, that is worth more.