Baidu released "DuMate," an Agent harness designed to cut token consumption by 75% versus the previous generation, with no quality loss on Agent benchmarks. The result is one of the largest "token efficiency" improvements in the industry, achieved entirely through "Harness engineering" — i.e., changes in how the Agent orchestrates the LLM, not the LLM itself.
The "Harness engineering" insight: most Agent costs come from the LLM, but the LLM is not the only place to optimize. The "harness" — the orchestration code that decides what to ask the LLM, how to format the prompt, how to handle errors — is responsible for 60-80% of token consumption. Optimizing the harness can save more than optimizing the LLM.
DuMate's optimizations include: (1) "smart context compression" — long context is summarized into a compact representation, reducing the prompt size by 60%; (2) "tool result caching" — repeated tool calls are cached, avoiding redundant computation; (3) "speculative execution" — the harness predicts the next step and pre-fetches the necessary data; (4) "adaptive sampling" — the harness uses lower temperature for "easy" steps and higher temperature for "hard" steps, reducing the number of retries.
The benchmark: on the SWE-Bench-Agent benchmark, DuMate hits the same accuracy as the baseline Agent, with 25% of the tokens. On the WebShop benchmark, the token reduction is 78%. The cost savings translate directly to API cost savings — a typical Agent task drops from $0.20 to $0.05.
The bigger takeaway: "Harness engineering" is becoming a real discipline. The "the LLM is the moat" assumption is breaking, and the "harness is the moat" view is gaining traction. For the industry, this means "Agent companies" need to invest in harness engineering, not just LLM access. The "best harness" can save 70-80% of token costs, which is a much larger lever than "the best LLM" (which typically gives 10-20% quality improvements).