TrustBound

TrustBound is an AI-assisted software engineering platform designed to make autonomous code generation safer, more transparent, and easier to control. As AI agents become increasingly capable of writing and modifying code, a critical challenge remains: how can we ensure that AI-generated changes are executed within trusted boundaries? TrustBound addresses this challenge by combining AI-powered project planning, human approval, isolated sandbox execution, and auditable evidence into a structured development workflow. Users can import Python projects, describe their development goals in natural language, and use LLMs powered by Vultr Inference to generate implementation plans and propose code modifications. Before execution, users review and approve the proposed scope and permissions. The Executor Agent then generates code changes, which are executed inside an isolated Rootless Docker environment with enforced resource limits and restricted network access. TrustBound records execution results, code differences, security checks, and cleanup evidence, allowing users to examine what happened rather than relying solely on AI-generated explanations. The platform also includes recorded demonstrations of adversarial verification and bounded agent recovery, while the current live Workspace focuses on controlled code execution and security evidence. Our vision is simple: AI should be able to write code, but trust must come from verifiable execution, explicit authorization, and transparent evidence.