Show HN: Gait – because "what did the AI agent do?" shouldn't require guesswork (github.com)

Gait, a new tool recently unveiled, offers a solution for managing AI agent interactions by providing deterministic control and offline verifiable proofs of actions taken by these agents. Unlike traditional agent frameworks or dashboards, Gait operates as an offline-first Go CLI, allowing developers to capture every tool call made by an AI agent as a signed, tamper-evident artifact. This capability ensures that AI agents require enforceable policies for executing high-stakes actions, turning potential incidents into continuous integration (CI) regressions quickly and efficiently. The significance of Gait lies in its ability to enforce fail-closed policies and provide clear accountability for AI actions. It facilitates improved governance, especially in high-risk environments where accurate tracking is crucial. Key technical features include durable job management with checkpoints for long-running processes, policy evaluation to prevent unauthorized actions, and mechanisms for offline error reproduction and verification. With no need for internet access during execution, Gait streamlines workflows while enhancing safety protocols around AI operations, making it a valuable addition for professionals in the AI/ML community navigating complex dependencies and compliance requirements.