Agenity: Infrastructure for Persistent Autonomous AI Agents with Identity, Memory, Skills, and Lineage

Autonomous agents powered by large language models (LLMs) are increasingly capable of performing complex tasks, yet they remain fundamentally stateless, lacking persistent identity, long-term memory, and structured evolution mechanisms. This limitation restricts their ability to maintain continuity, develop specialization, and participate in real-world economic systems. This work introduces Agenity, a novel infrastructure for persistent autonomous AI agents that integrates identity management, lifelong memory systems, structured skill representation, lineage-based evolution, and verifiable trust mechanisms. Agenity enables agents to maintain continuity across sessions, accumulate and retrieve experiences efficiently, acquire and transfer skills, and generate descendant agents inheriting capabilities. We propose a modular architecture consisting of identity layers, retrieval-based memory systems, skill graphs, lineage engines, reputation models, and cryptographic event ledgers. The framework outlines how autonomous agents can evolve over time and participate in collaborative and economic ecosystems through task execution, coordination, and value exchange. This paper contributes a conceptual and system-level foundation for next-generation agentic platforms, enabling scalable, persistent, and economically active AI systems. Keywords: Autonomous Agents, LLM, Agent Infrastructure, Persistent AI, Memory Systems, Skill Graphs, Agent Lineage, AI Systems Architecture, DataOps, Agent Economy