Introducing Harvey II

This post introduces Harvey II, which significantly enhances the AML AI Detection Systems by integrating matter/project context inheritance and persistent user memory into agent workflows. Agents now operate within a 'Space' that encapsulates all relevant context (documents, permissions, history) for a given matter, eliminating the need for repeated setup. The 'Memory' feature allows agents to learn and apply user-specific preferences and team templates, personalizing the AI's output. This is underpinned by the new Harvey Tenet model, a frontier-level, legal-specific model designed for cost-efficient, long-horizon legal reasoning. The post details how these components combine to enable agents to handle more substantive legal work by starting with pre-existing context and learned preferences.