Abstract

Traditional Relational Database Management Systems (RDBMS) have long relied on static schemas, explicit relational mappings, and rigid migration processes. However, in the era of autonomous AI agents, these legacy constraints introduce unnecessary friction. This paper introduces "Microforce," a novel architectural paradigm that eliminates predefined relations and structural migrations entirely. By leveraging a multi-dimensional Key-Value Store (KVS), Microforce treats data, schemas, and execution logic as independent, transparent layers. Instead of utilizing procedural business logic (e.g., explicit iterations and conditional branching), the system employs "Transparent Observation." In this process, AI agents project an intent (a target key) across these layers, allowing the optimal data state to converge and crystallize instantly without explicit structural joins. We demonstrate that this non-von Neumann approach significantly reduces architectural overhead, and we provide a production-ready blueprint available as an open-source repository.