Agent Harness Matters: Rust-based Ante Aces 10 Terminal-Bench Tasks
VoidAsuka · x · 2026-08-12
When building AI agents, the choice of the Agent Harness (the execution framework) can be just as critical as the underlying model. Antigma Labs recently published an experiment where they fixed the model (deepseek-v4-flash), the Daytona sandbox, and a set of 10 Terminal-Bench 2.1 tasks, and then swapped out the agent framework to measure the impact.
The results clearly show that the harness changes the outcome:
- Ante (written in Rust): Passed all 10 tasks, achieving the highest pass rate.
- Ante-short (using --short-prompt): Passed 9 tasks but proved to be faster, cheaper per pass, and more efficient with tool calls.
- Pi, OpenCode, and Hermes: Each passed 7 tasks.
While 10 tasks are a relatively small sample size, the test successfully demonstrates that heavily optimized frameworks for memory and CPU usage—like the Rust-based Ante—can squeeze significantly better performance out of the same model.
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