Show HN: Agent Audit Kit v0.1 – deterministic replay + stress for LLM agents (github.com)

The release of the Agent Audit Kit (AAK) v0.1.0-e3 marks a significant advancement in the field of AI and machine learning, particularly for the evaluation of large language model (LLM) agents. This open-core toolkit facilitates deterministic capture, replay verification, and stress-testing of AI agents, enabling developers to create portable evidence bundles that can be reliably verified across different environments. Users can execute a “golden run” that validates functionality and performance on a cold machine, providing key hash verifications to ensure consistency. This toolkit is particularly valuable for researchers and developers working with LLMs, as it streamlines the process of debugging and validating agent behavior. By offering a standardized mechanism for replaying training environments and stress-testing agents, AAK v0.1.0-e3 lays the groundwork for improved accountability and transparency in AI systems. However, it is important to note that while it enhances verification processes, the toolkit does not provide compliance certification nor does it assert determinism of hosted LLM outputs, thereby positioning itself as a forensic rather than a preventive measure in AI operations.