Neurosymbolic AI is a rising technique that aims to solve the hallucination problem. It’s a hybrid approach that marries gen AI’s neural networks for pattern recognition with symbolic reasoning’s logic that can prove whether outputs are correct, with mathematical precision. Amazon has become one of neurosymbolic AI’s most prominent adopters. Its Automated Reasoning Group, built tools to verify security policies in the AWS cloud. Those methods now underpin new systems such as the Vulcan warehouse robots, which combine neural networks for perception with automated reasoning for precise planning. Amazon and Imandra are advancing the approach in parallel, Amazon with warehouse robots and shopping assistants, Imandra with finance, code verification and regulatory compliance. Imandra built a mathematically precise language for specifying the FIX (Financial Information eXchange) protocol. Instead of relying on hundreds of pages of PDFs, clients can now use automated reasoning to ensure precise communication each way. To scale its approach, Imandra launched Imandra Universe, a platform it describes as the first marketplace for neurosymbolic agents. It hosts symbolic reasoning engines specialized in domains from geometry to logistics. A “Reasoner Gateway” lets developers plug them into agent frameworks, augmenting AI systems with logical checks. One flagship tool, Code Logician, targets the flood of AI-generated code. Once installed in coding assistants like Cursor, Code Logician builds a mathematical description of what the AI generated code is doing and use Imandra to verify it. Passmore said around 60% of AI-generated code contains bugs and Code Logician can make 96% of the code correct within three iterations. Imandra is expanding Code Logician beyond Python to Java and even COBOL, reflecting enterprise demand for code migration. It is also developing agents for geometry reasoning, among other pursuits.