让AI agent跨会话安全共享记忆，提升协作效率与合规性。
SAMEP: A Secure Protocol for Persistent Context Sharing Across AI Agents
- 构建分布式记忆库，用向量搜索实现语义匹配。
- 减少73%重复计算，上下文相关性提升89%。
- 适合需跨代理协作的医疗、软件开发等高合规场景。
当前AI代理架构存在记忆短暂的问题，难以在会话间及代理边界间有效协作与知识共享。我们提出SAMEP（安全代理记忆交换协议），一种新型框架，支持AI代理间持久、安全且可语义搜索的记忆共享。该协议解决三大挑战：(1) 跨会话持久化上下文保存，(2) 带细粒度访问控制的安全多代理协作，(3) 高效语义化历史上下文发现。SAMEP采用基于向量的语义搜索分布式记忆仓库，结合AES-256-GCM加密访问控制，并兼容现有代理通信协议（MCP、A2A）。在多代理软件开发、符合HIPAA要求的医疗AI以及多模态处理流水线等多领域验证中，实验显示冗余计算减少73%，上下文相关性评分提升89%，且完全满足审计追踪等监管要求。SAMEP实现了持久、协作式AI代理生态的新范式，同时保障安全与隐私。
原文摘要 · Abstract (English)
Current AI agent architectures suffer from ephemeral memory limitations, preventing effective collaboration and knowledge sharing across sessions and agent boundaries. We introduce SAMEP (Secure Agent Memory Exchange Protocol), a novel framework that enables persistent, secure, and semantically searchable memory sharing among AI agents. Our protocol addresses three critical challenges: (1) persistent context preservation across agent sessions, (2) secure multi-agent collaboration with fine-grained access control, and (3) efficient semantic discovery of relevant historical context. SAMEP implements a distributed memory repository with vector-based semantic search, cryptographic access controls (AES-256-GCM), and standardized APIs compatible with existing agent communication protocols (MCP, A2A). We demonstrate SAMEP's effectiveness across diverse domains including multi-agent software development, healthcare AI with HIPAA compliance, and multi-modal processing pipelines. Experimental results show 73% reduction in redundant computations, 89% improvement in context relevance scores, and complete compliance with regulatory requirements including audit trail generation. SAMEP enables a new paradigm of persistent, collaborative AI agent ecosystems while maintaining security and privacy guarantees.
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