Show HN: AgentCarousel – behavioral tests for AI agents, with signed evidence (github.com)

AgentCarousel has launched a new testing framework designed for AI agents, enabling developers to conduct behavioral tests in continuous integration (CI) environments. This tool allows for the creation of YAML-based test cases that outline expected behaviors for AI interactions, verifying outputs against predefined standards. The significance of AgentCarousel lies in its ability to enhance the reliability of AI agents by ensuring they adhere to specified behaviors, thereby allowing teams to catch regressions and maintain compliance with various regulatory frameworks such as NIST and the EU AI Act. The framework facilitates a comprehensive evaluation process, automating the generation and validation of test cases while utilizing an LLM (Language Model) as a judge to score performance against a rubric. Key features include cryptographically signed exportation of testing data for compliance audits, as well as the ability to benchmark multiple models simultaneously based on pass rates and efficiency metrics. By incorporating structured behavioral testing into the development lifecycle, AgentCarousel supports engineering teams in shipping robust AI solutions with documented compliance, potentially reducing risks associated with deployment and regulatory challenges.