Forking data for AI agents: The missing primitive for safe, scalable systems

• Tigris has introduced a solution aimed at addressing agent failures in AI systems, which often arise from inconsistent state. The company offers immutable storage, snapshots, and forks to facilitate deterministic and reproducible AI workflows.
• This development is significant as it enhances the reliability and scalability of AI systems, potentially leading to safer implementations in various applications, thereby supporting the growth of AI technologies within the industry.

— via World Pulse Now AI Editorial System