## The Memory Ceiling of Agentic Orchestration As autonomous software agents progress from single-turn prompts to long-horizon workflows spanning thousands of tool executions, standard tokenization pipelines create crippling memory and latency overheads. When an agent searches a codebase, reads a hundred files, and generates diffs, naive string duplication across JSON-RPC boundaries wastes substantial clock cycles. ### The Pipeline Architecture Our benchmark architecture implements shared-memory buffers and zero-copy byte slices for context propagation: - Memory-Mapped Vector Stores: Read directly from disk caches without heap allocation. - Incremental KV-Cache Reuse: Retain stable prefix prompts across consecutive tool executions. - Binary Streaming Protocols: Swap heavy JSON tool responses for compact Arrow/Protobuf IPC formats. The results show that optimizing the data plane of LLM orchestrators is just as important as the underlying weights.