PrimeAgentOrchestrator: Memory-Primed Agent Spawning for Personal AI Infrastructure
PrimeAgentOrchestrator: Memory-Primed Agent Spawning for Personal AI Infrastructure
PrimeAgentOrchestrator：面向个人 AI 基础设施的记忆预加载智能体生成系统
Abstract: Large language model (LLM) coding agents start each session with an empty context window, discarding accumulated knowledge from prior work. We present PrimeAgentOrchestrator (PAO), a system that spawns new instances of Claude Code — Anthropic’s terminal-based coding agent — pre-loaded with relevant memories compiled from the user’s existing personal databases.
摘要： 大型语言模型（LLM）编程智能体在每次会话开始时，其上下文窗口都是空的，这意味着它们会丢弃之前工作中积累的知识。我们提出了 PrimeAgentOrchestrator (PAO)，这是一个能够生成 Claude Code（Anthropic 基于终端的编程智能体）新实例的系统，它能在生成时预加载从用户现有个人数据库中编译出的相关记忆。
At spawn time, PAO queries two independently-operated memory backends in parallel (a PostgreSQL entity-observation database and a Cloudflare Worker semantic search index), fuses results using backend-specific retrieval strategies, and delivers the compiled briefing via filesystem injection that exploits the host agent’s configuration auto-read behavior.
在生成实例时，PAO 会并行查询两个独立运行的记忆后端（一个 PostgreSQL 实体观察数据库和一个 Cloudflare Worker 语义搜索索引），利用针对后端优化的检索策略融合结果，并通过文件系统注入的方式交付编译后的简报，从而利用宿主智能体的配置自动读取行为。
PAO manages the full agent lifecycle including trust pre-seeding, readiness polling with error detection, and adaptive terminal text injection. We report on four months of regular deployment (December 2025 through March 2026) as an experience report, documenting three generations of context delivery mechanisms, the failure modes that motivated each redesign, and the engineering tradeoffs of bridging heterogeneous memory systems rather than building a unified one.
PAO 管理智能体的完整生命周期，包括信任预植入、带错误检测的就绪状态轮询以及自适应终端文本注入。我们以经验报告的形式记录了四个月（2025 年 12 月至 2026 年 3 月）的常规部署情况，详细说明了三代上下文交付机制、促使每次重新设计的故障模式，以及在连接异构记忆系统而非构建统一系统时所做的工程权衡。
Paper Details:
- Authors: Myron Koch (Peak Summit Labs)
- Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
- arXiv ID: 2608.20342
论文详情：
- 作者： Myron Koch (Peak Summit Labs)
- 学科： 人工智能 (cs.AI)；多智能体系统 (cs.MA)
- arXiv ID： 2608.20342