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

TL;DR

SAMEP introduces a secure, persistent memory sharing protocol for AI agents, enabling effective collaboration, semantic search, and privacy compliance across diverse applications.

Contribution

It presents a novel framework combining secure cryptographic memory sharing, semantic search, and standardized APIs for persistent multi-agent collaboration.

Findings

73% reduction in redundant computations

89% improvement in context relevance

Full compliance with privacy regulations

Abstract

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.