Don't pay for deep thinking on easy turns

Reasoning effort is mostly output tokens, and output tokens are usually the expensive direction, so the effort knob is often the single biggest lever on an agent's bill, bigger than switching models. A cheap model at maximum effort can out-spend a mid model at low effort. Your job is to set a sane default, define when escalation is earned, and validate the config actually says what the user thinks it says.

Steps

1. Confirm the user's harness supports per-task effort control. The worked example is Hermes (levels: none, minimal, low, medium, high, xhigh, switchable at runtime with /reasoning xhigh, no restart); Claude Code, the Claude API's thinking budgets, and most agent runtimes have an equivalent. If theirs doesn't, effort throttling means routing between the same model's endpoints at different configured efforts.
2. Set the default one notch below the maximum the user has been running, not at the bottom. The reference setup (as of 2026-06): DeepSeek V4 Flash at $0.098/M in, $0.196/M out on OpenRouter; default high, reserve xhigh for turns that earned it. Check current prices before quoting.
3. Define "earned" concretely so escalation is a rule, not a mood: a failed first attempt, a task the user tagged hard, a step with irreversible consequences. Everything else runs at the default.
4. Run the proof below; it validates the config parses and the effort level is one the harness actually accepts (a typo'd level silently falls back to a default you didn't choose). The example uses ruby's stdlib YAML parser; if ruby isn't on the machine, any YAML parser serves, since the point is proving the file parses and the level is a real one.
5. After a week, check what fraction of the bill is output tokens at each effort level. If xhigh turns are more than ~10-20% of traffic, either the work is genuinely hard (fine, see advisor-call-budget for the next lever) or the escalation rule is too loose.

Prove it