# Offloading bounded decisions cut agent LLM calls by 29.6% on 360 SOP-Bench cases

> Source: <https://discuss.huggingface.co/t/offloading-bounded-decisions-cut-agent-llm-calls-by-29-6-on-360-sop-bench-cases/190420#post_1>
> Published: 2026-10-12 16:47:16+00:00

Disclosure: I built Proceda, the SOP-execution harness used here, and ran these experiments.

I wanted to see how much work an agent could hand to a decision model when the answer is narrower than general-purpose text generation. I tested Qwen 3.8 27B with hosted Jev on 360 SOP-Bench cases across referral abuse detection, email intent, and patient intake.

I tried two insertion points:

With both changes enabled:

There is a tradeoff worth separating out: completion checking alone saved 23.8% of LLM calls but finished 356/360 tasks correctly, versus 357/360 for the baseline. The combined result above is from this particular 360-case subset; it does not establish a general accuracy improvement. Including the final structured-plan preparation cost also reduces the estimated cost saving to 24.6%.

The useful architectural pattern is to supply evidence, define allowed choices, validate the result, and keep an LLM fallback. The implementation and experiment reports are public:

Which decisions in your agents have you successfully offloaded, and how do you validate confidence thresholds before allowing them to bypass the main LLM?