Show HN: A gate for AI agents that ships a board of its own worst flaws (github.com)

A new tool called Lotor has been introduced, designed to enhance accountability for AI agent sessions by creating a signed, tamper-evident log that records agents' actions, including files accessed and costs incurred, all stored locally on the user's machine. This innovation addresses the growing concern that as AI agents proliferate, the need for reliable and verifiable records of their actions becomes crucial. By keeping the log local, Lotor ensures that users maintain control over the integrity of their records, rather than relying on third-party vendors who may manipulate logs without oversight. Lotor's significance lies in its ability to separate the roles of the agent and the record keeper, fostering a trustworthy environment for AI interactions. Unlike conventional cloud-based accountability tools that risk centralizing sensitive information, Lotor’s architecture promotes transparency by granting users a physical record that cannot be altered by the AI itself or its vendors. With features such as session accountability, detailed failure reports, and the ability to gate high-stakes actions, Lotor provides an essential safeguard for organizations navigating the complexities of AI deployment while ensuring that they retain ownership of their operational history.