Agent bottlenecks are in the loop, not the model, cutting costs by 30%
Background-Job-862 · reddit · 2026-08-27
Comparing agent runtimes with a fixed Claude Opus model and identical success rates (11/14 tasks), the author found massive performance gaps: the fastest runtime was 2.5x quicker (39 vs 96 mins), used 3x fewer tokens (3.85M vs 13M), and cost 30% less than the slowest.
The differences stemmed from "boring runtime stuff": how much system prompt/tool context is resent per turn, how tool outputs accumulate, and the aggressiveness of loop exploration/retries. This suggests agent benchmarks measure the combination of model + harness + prompting + tool loop, not just the model.
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