Auditra

AI-generated software is becoming faster to produce, but speed creates a critical problem: how do we know AI-written code is actually safe to release? Traditional testing can miss adversarial edge cases, while asking the same AI that generated the code to judge whether it is correct creates a circular trust problem. Auditra is a zero-trust verification fabric for AI-generated software. Its core principle is simple: AI proposes; Auditra verifies. Auditra provides an independent verification layer between AI-generated code and deployment. It uses deterministic behavioral oracles, adversarial and boundary scenarios, AST/security validation, a lightweight isolated execution sandbox, mutation testing, and automatic rollback to evaluate proposed changes. The AI never controls the verification rules or decides whether its own code is correct. The platform currently demonstrates this across four intentionally vulnerable services: Tax Router, Billing Engine, Ledger Sync, and Fraud Detector. Auditra runs 58 verification scenarios across these services and uses 6 deliberate code mutations to demonstrate that its verification layer can detect meaningful behavioral changes. The target users are software developers, AI coding-agent users, engineering teams, and organizations adopting AI-assisted software development. Developers can interact with Auditra through its verification workflow or through IBM Bob 2.0 via MCP. Bob can request verification of an individual target or the entire system, receive deterministic feedback, and use that feedback to repair the code. Auditra then independently re-verifies the result. What makes Auditra different is its separation of generation and verification. Instead of making an AI agent both the programmer and judge, Auditra establishes an independent trust boundary around the generated code. A release is approved only when the defined behavioral and security checks succeed; otherwise, the release is blocked or the applied patch is rolled back. The result is a practical verification layer designed to make AI-assisted development more trustworthy, observable, and release-aware without removing the speed and flexibility of AI coding agents.