Beyond Automation: Achieving Coherent Intelligence with Shared Memory Architectures

Abstract

Enterprise AI is rapidly shifting from single-model deployments to multi-agent systems that coordinate discovery, analysis, validation, and synthesis across complex workflows. Yet in practice, agent brilliance often collapses into collaboration chaos insights vanish, work repeats, and reasoning drifts. This paper advances a Shared Memory Architecture (SMA) for multi-agent AI that elevates memory from a feature to a foundational system design. By implementing a context graph with semantic embeddings, lineage, confidence scoring, and validation links, organizations can transform disconnected automations into coherent, auditable intelligence. The approach demonstrates measurable improvements reducing redundant compute, stabilizing reasoning, and enabling full decision traceability while aligning with regulatory and governance expectations. The paper outlines architecture, implementation patterns, governance guardrails, and adoption playbooks, with use cases spanning trading, retail, supply chain, and integrated quality engineering. The outcome: systems that remember, reason, and regulate themselves, unlocking scalable enterprise value under uncertainty.