threshold()

The soak-to-verify outcome contract — iterate on commodity hardware until a target rate is verified on the escalation tier.

Every other primitive sells measurement. roborama.threshold() sells an outcome: iterate until verified at the target. You declare the rate you need, the task, and a monthly cap; Roborama manages the embodiment ladder underneath and calls your webhook when the number is real.

The call

import roborama  # reads ROBORAMA_API_KEY from the environment

contract = roborama.threshold(
    policy_stream=roborama.PolicyStream(webhook="https://acme.ai/ckpt"),
    target={"success_rate": 0.99, "ci": 0.95, "task": "bin_pick@v1"},
    iterate_on="soak",               # commodity tier, e.g. nori-a3 pods
    escalate_to="g1-edu-pro@fw2.3",  # verification tier on crossing
    monthly_cap_usd=12_000,
)

contract.on_verified(webhook="https://acme.ai/release-gate")
print(contract.id, contract.status)

The contract, field by field

policy_stream is the input side: a PolicyStream whose webhook receives checkpoints straight from your training loop. Every checkpoint you push enters the evaluation queue — no human packaging step between a training run and physical episodes.

target={"success_rate": 0.99, "ci": 0.95, "task": "bin_pick@v1"} is the outcome being bought: the contract completes when a checkpoint is verified at 0.99 on bin_pick@v1 with 95% confidence, not when a point estimate grazes the number.

iterate_on="soak" sets where the grinding happens: the commodity tier — nori-a3 pods at $7/robot-hour — cheap enough to evaluate checkpoints continuously as training produces them.

escalate_to="g1-edu-pro@fw2.3" is the verification tier. When a checkpoint crosses the target on soak — canonically 0.992 (n=1188, ci95 0.985–0.995) — it escalates to the target embodiment, where the verdict that gates your release is actually measured.

monthly_cap_usd=12_000 is a hard cap, metered live. The contract pauses at the cap with monthly_budget_exceeded and resumes next period; it never quietly overruns.

The embodiment ladder

The ladder is the price structure. Training noise — checkpoints that were never going to cross — burns $7/robot-hour pods, not verification-tier humanoids. Only a checkpoint that earns escalation touches g1-edu-pro@fw2.3. You stop choosing between statistical rigor and iteration volume, because the cheap tier buys volume and the expensive tier buys the verdict.

Agent-native by design

No human is required anywhere in this loop. A training run pushes a checkpoint at 3 a.m.; soak episodes accumulate; threshold.crossed fires when a checkpoint crosses; the escalation run schedules itself; and the URL you registered with on_verified hears the physical verdict — programmatically, with n and interval attached. Wire that webhook to your release gate and the loop closes: train, push, verify, ship, without a meeting.

Tier rates and the burst/standard/soak multipliers are on /pricing/; caps and metering are covered in billing and budgets.