Abstract As Large Language Model (LLM) agents increasingly integrate numerous external systems, they suffer from Tool Space Interference (TSI), a phenomenon causing context bloat, attention dilution, and degraded reasoning accuracy. In this paper, we introduce the Agent-as-a-Tool paradigm—an evolutionary, practical implementation of the recently proposed Self-Optimizing Tool Caching Network (SOTCN) and Federated Context-Aware Routing Architecture (Federated CARA). By leveraging Retrieval-Augmented Generation (RAG) to dynamically discover and assemble stateful, autonomous sub-agents on the fly, this architecture completely eliminates TSI, enforces Zero-Trust execution boundaries, and achieves infinitely scalable AI orchestration.…