标题： SAMEP：一种用于AI代理之间持久上下文共享的安全协议 显示英文标题

标题： SAMEP: A Secure Protocol for Persistent Context Sharing Across AI Agents

摘要： 当前的AI代理架构受到短暂记忆限制的困扰，这阻碍了跨会话和代理边界的有效协作和知识共享。 我们引入了SAMEP（安全代理记忆交换协议），这是一种新框架，能够在AI代理之间实现持久、安全且语义可搜索的记忆共享。 我们的协议解决了三个关键挑战：(1) 跨代理会话的持续上下文保留，(2) 具有细粒度访问控制的安全多代理协作，以及(3) 高效的语义相关历史上下文发现。 SAMEP实现了基于向量的语义搜索分布式记忆库，加密访问控制（AES-256-GCM），以及与现有代理通信协议（MCP，A2A）兼容的标准API。 我们在包括多代理软件开发、符合HIPAA的医疗AI以及多模态处理流水线在内的多个领域中展示了SAMEP的有效性。 实验结果表明，冗余计算减少了73%，上下文相关性评分提高了89%，并且完全符合包括审计跟踪生成在内的监管要求。 SAMEP使持久的、协作的AI代理生态系统成为可能，同时保持安全和隐私保证。 显示英文摘要

摘要： 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.