Researchers publish an empirical study of coding-agent harness design
An empirical study of coding-agent harness design — it evaluated 176 harness configurations across 4 models. ① The smaller the token budget, the more decisive context management becomes, and its main effect is preventing overflow failures; ② combining rule-based removal with LLM summarization works best, while letting the agent recover removed content brings almost no gain; ③ planning improves accuracy for weak models but mainly cost for strong ones; ④ weak models benefit from predefined tool interfaces, but strong models do better on both performance and efficiency when given only bash (especially on terminal-centric tasks). Trajectory-level analysis explains each mechanism.
Why it matters
Backs "the stronger the model, the thinner the harness should be" with measurements across 176 configurations. It marks the point where design guidance for axis ⑤, loop and harness engineering, moves from rules of thumb to evidence.
Source — Primary sources first; secondary reports are labelled “Press”
Paperarxiv.orghttps://arxiv.org/abs/2609.20804Added in Week 39, 2026 · See that week