LLM agents execute multi-step workflows that mutate external state through tools. Common orchestrators treat tool return as the settlement trigger, so faults, speculation, and concurrent agents can leave partial effects, losing-branch residue, stale writes, or irreversible sends. Correct settlement needs two facts that retries, checkpoint replay, locks, and compensation each conflate: which effects must settle together, and when earlier conflicting work is exhausted. Atomix makes this split explicit with progress-aware transactions. The runtime records reads and effects during execution, seals a transaction when its footprint is complete, and commits only after per-resource frontiers show that no earlier conflicting work can still arrive. Commit is final settlement: Atomix releases bufferable effects, accepts reversible external effects as final, and lets irreversible effects leave the gate. Abort suppresses unreleased effects and compensates externalized reversible effects where possible. On representative agent workloads, this composition improves clean recovery under injected faults, isolates contending and speculative work, and prevents correctly classified irreversible actions from leaking; microbenchmarks show microsecond-scale wrapper overhead relative to tool latency.

翻译：大语言模型智能体通过工具执行多步骤工作流，这些工作流会改变外部状态。常见的编排器将工具返回视为结算触发器，因此故障、推测和并发智能体会留下部分效果、丢失分支残留、过时写入或不可逆发送。正确的结算需要两个事实（重试、检查点重放、锁和补偿各自混淆了这两个事实）：哪些效果必须一起结算，以及较早的冲突工作何时耗尽。Atomix 通过进度感知事务使这种分离变得显式。运行时在执行期间记录读取和效果，当其足迹完成时密封事务，并且仅在每个资源的前沿显示没有较早的冲突工作仍然可以到达后才提交。提交是最终结算：Atomix 释放可缓冲效果，将可逆外部效果视为最终效果，并让不可逆效果离开门。中止抑制未发布的效果，并在可能的情况下补偿外部化的可逆效果。在代表性智能体工作负载上，这种组合在注入故障下改善了干净恢复，隔离了竞争和推测工作，并防止了正确分类的不可逆动作的泄漏；微基准测试显示，与工具延迟相比，包装器开销为微秒级。