Show HN: Agentrial – pytest for AI agents with statistical rigor (github.com)

Agentrial, a new statistical evaluation framework for AI agents, has been introduced, offering a testing approach akin to pytest, but tailored for non-deterministic AI systems. This tool allows developers to rigorously assess AI agents by running multiple trials and utilizing Wilson confidence intervals, ensuring that results reflect reliability rather than isolated successes. With features like trajectory analysis to pinpoint failure sources, accurate cost tracking from actual API usage, and CI/CD compatibility that can block pull requests based on reliability metrics, Agentrial is set to enhance the robustness of AI agent testing. The significance of Agentrial for the AI/ML community lies in its emphasis on statistical rigor and comprehensive evaluation methods, which are critical as AI systems become increasingly complex and permeate various applications. By providing detailed insights into test performance—including pass rates, confidence intervals, costs, and latencies—developers can fine-tune agents more effectively. Additionally, the integration with GitHub Actions simplifies workflow automation, making it easier to maintain high standards of reliability and performance in AI deployments. With support for various agents and a user-friendly installation, Agentrial is positioned to be an essential tool for AI developers focusing on operational efficiency and quality assurance.