The future will be millions agents running task everyday? (github.com)

A new project has launched a controlled benchmark for evaluating the runtimes of coding agents across four programming languages: C++, Python, TypeScript, and Rust. By isolating the runtime environment, the benchmark measures metrics such as memory footprint, concurrency behavior, and overhead under load while executing a sequence of tasks defined in the HumanEval dataset. Notably, this benchmark focuses on the orchestration of multiple agents running concurrently, rather than solely comparing the language models themselves, which adds significant value to developers aiming to understand performance implications in real-world applications. The project's significance lies in its potential to standardize comparisons for agent runtimes, which have previously been hampered by varying hardware and model configurations. Through a methodical approach—performing identical tasks using the same model and environment—it establishes a clear benchmark for developers choosing a language stack for AI agent deployments. Early results showcase the C++ implementation's peak memory usage at around 93 MiB for 100 concurrent agents, with a pass rate of 96% on the first attempt when applying self-correction, setting a high bar for the expectations of efficiency and effectiveness in AI-driven coding agents.