X @Avi Chawla

AI Agent & Database Evolution - AI agents are challenging the traditional database model designed for human interaction [1] - The industry recognizes the need for databases to adapt to the requirements of AI agents, which differ significantly from human users [1] Agentic Postgres Features - TimescaleDB introduces Agentic Postgres, an agent-ready version of Postgres designed to address the challenges posed by AI agents [2] - Agentic Postgres enables instant database branching, facilitating parallel agent evaluations, safe experiments, migrations, and isolated testing with minimal cost and time [2] - It includes a built-in MCP server, offering schema guidance, best practices, and secure, structured access to Postgres for agents, aiding in informed migrations [3] - Hybrid search (vector search and BM25) is integrated, allowing agents to directly retrieve data within the database [3] - The database is memory-native, providing a persistent context for agent evolution [3] AI Agent Requirements - AI agents require the ability to branch endlessly, run multiple experiments concurrently, and operate within isolated, contextualized, and secure sandboxes [4]