sksaddam

ActionProof helps developers answer a critical question about AI agents: does the action being executed still match what the user approved? An agent might request approval for a purchase, but the price can change before execution. A document recipient can change after review. An API timeout can leave the agent uncertain whether an operation succeeded, creating a risk of duplicate actions. ActionProof connects an AI agent to a controlled execution gateway. The agent prepares an action, the user reviews its exact details, and deterministic backend checks validate approval immediately before execution. Approval is bound to a versioned action snapshot. If material details change, the action requires fresh review. The application brings together an agent workspace, approval controls, controlled failure scenarios, and an evidence timeline. Users can inspect proposed actions, approval decisions, changes, execution outcomes, and committed effects. Its sandbox workflows demonstrate how agent behavior changes when approval enforcement is enabled. The intended users are engineers building agents that interact with business systems. ActionProof makes stale approvals and uncertain execution outcomes visible and reproducible, helping teams investigate failures before connecting agents to consequential operations. The prototype uses sandbox transactions. Its guarantees are limited to the implemented execution boundary; external integrations would require provider-specific safeguards and reconciliation.