Orchestrating Decentralized Knowledge Graphs for Autonomous Multi‑Agent Retrieval‑Augmented Generation Systems
Introduction The convergence of three once‑separate research strands—knowledge graphs, decentralized architectures, and retrieval‑augmented generation (RAG)—has opened a new frontier for building autonomous multi‑agent systems that can reason, retrieve, and synthesize information at scale. In a traditional RAG pipeline, a single language model queries a static corpus, retrieves relevant passages, and augments its generation with that context. While effective for many use‑cases, this monolithic approach struggles with: Data silos: Knowledge resides in isolated databases, proprietary APIs, or edge devices. Scalability limits: Centralised storage becomes a bottleneck as the graph grows. Trust and provenance: Users need verifiable sources for generated content, especially in regulated domains. A decentralized knowledge graph (DKG) solves the first two problems by distributing graph data across a peer‑to‑peer (P2P) network, often leveraging technologies such as IPFS, libp2p, or blockchain‑based ledgers. When combined with autonomous agents—software entities capable of planning, executing, and negotiating tasks—the system can orchestrate retrieval, reasoning, and generation across many nodes, each contributing its own expertise and data. ...