Data agents, empowered by Large Language Models (LLMs), introduce a new paradigm in transaction processing. Unlike traditional applications with fixed patterns, data agents run online-generated workflows that repeatedly issue SQL statements, reason over intermediate results, and revise subsequent plans. To ensure data consistency, these SQL statements issued by an agent should be integrated into a transaction, referred to as agentic transactions. Agentic transactions exhibit unforeseen characteristics, including long execution times, irregular execution intervals, and non-deterministic access patterns, breaking the assumptions underlying concurrency control (CC) (e.g., short-lived, predefined). Traditional CC schemes, which rely on fixed policies, fail to capture such dynamic behavior, resulting in inadequate performance. This paper introduces ATCC, an adaptive Concurrency Control for Agentic Transactions. ATCC continuously monitors and interprets the runtime behavior of each agentic transaction, evaluates its interactive phases, and dynamically adapts optimistic or pessimistic execution for each transaction. To ensure precise timing for adaptive switches, ATCC employs a reinforcement learning-based policy to balance immediate blocking against future abort costs. Additionally, to mitigate contention-induced tail latency and wasted reasoning cost caused by abort, a cost-aware priority-based lock scheduling is integrated to prioritize expensive or latency-sensitive transactions. Experimental results under agentic-like YCSB and TPC-C workloads demonstrate that ATCC improves the throughput of agentic transactions by up to four orders of magnitude and reduces tail latency by up to 90% compared to state-of-the-art CC schemes.

翻译：由大型语言模型（LLM）驱动的数据智能体为事务处理引入了新的范式。与传统应用程序具有固定模式不同，数据智能体运行在线生成的工作流，这些工作流会反复发出SQL语句、对中间结果进行推理并修订后续计划。为确保数据一致性，智能体发出的这些SQL语句应被整合到一个事务中，称为智能体事务。智能体事务展现出不可预见的特性，包括长执行时间、不规则执行间隔和非确定性访问模式，这打破了并发控制（CC）所依赖的假设（例如短时、预定义）。依赖固定策略的传统CC方案无法捕捉此类动态行为，导致性能不足。本文提出ATCC，一种面向智能体事务的自适应并发控制方案。ATCC持续监控并解释每个智能体事务的运行时行为，评估其交互阶段，并动态地为每个事务自适应选择乐观或悲观执行。为确保自适应切换的精确时机，ATCC采用基于强化学习的策略来平衡即时阻塞与未来中止成本。此外，为缓解由竞争引起的尾延迟以及因中止造成的推理成本浪费，ATCC集成了基于成本感知的优先级锁调度机制，以优先处理高成本或对延迟敏感的事务。在类智能体的YCSB和TPC-C负载下的实验结果表明，与最先进的CC方案相比，ATCC将智能体事务的吞吐量提升了高达四个数量级，并将尾延迟降低了高达90%。