State-Aligned Micro-Checkpointing (SAMC) for Reliable AI Agent Execution in Cloud LLM Systems

This paper presents State-Aligned Micro-Checkpointing (SAMC), a novel system designed to improve the reliability of AI agent execution in cloud-based large language model (LLM) environments. Modern AI agents integrate LLM inference with external tool interactions, but existing systems suffer from high recovery latency when failures occur, often requiring complete session recomputation. SAMC addresses this challenge by introducing a unified state model that captures three tightly coupled components: LLM engine state, agent workflow state, and environment interaction state. The proposed Micro-Checkpoint Capture Protocol (MCAP) enables lightweight and consistent state snapshots, while the Deterministic Replay Protocol (REP) ensures accurate and efficient recovery without recomputation. Experimental evaluation demonstrates that SAMC achieves constant-time recovery (O(1)) independent of context length, significantly outperforming traditional O(N) recovery methods. The system delivers up to 212× speedup, 100% KV cache hit rate, and near-zero overhead, making it suitable for real-world, latency-sensitive AI applications. This work contributes to advancing fault tolerance and reliability in distributed AI systems and provides a practical framework for scalable, production-grade AI agent deployment.