[Submitted on 26 May 2026]
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Abstract:A prevalent assumption in LLM agent deployment holds that more structured harnesses universally improve
reliability, and that higher-capability models need proportionally less structural guidance -- together
implying a monotone inverse relationship between model capability tier and optimal harness complexity. We
test this hypothesis through a controlled 432-run experiment crossing six models across four capability
tiers with three harness conditions (light, balanced, strict) on HEAT-24, a 24-task synthetic benchmark
with git-based workspace verification. Our results refute the monotone inverse relationship on two
fronts. First, for the frontier chat model evaluated (Gemini 2.5 Flash), increased harness verbosity
lowers VTSR by 29-38 percentage points -- a harness-complexity paradox. Second, for the frontier
reasoning model evaluated (Qwen3.5-122B, extended thinking enabled), strict harness achieves the highest
VTSR (91.7%) and the lowest latency, the opposite of the prediction. Within the constrained tier, a 2B
model (Gemma4:e2B) matches strong-open-tier stability at 91.7% across all harnesses. Because each tier is
represented by a single model in this study, these results should be interpreted as model-specific
observations; harness sensitivity appears non-monotone across the models evaluated, and depends
critically on model type (chat vs. reasoning). We introduce a six-label failure taxonomy showing that
format_violation dominates capable-model failures while wrong_file dominates low-capability failures, and
we derive practical tier-aware harness selection guidelines.
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