![[2602.14849] Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.14849] Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows
Summary
The paper presents Atomix, a runtime system designed to enhance the reliability of agentic workflows by implementing progress-aware transactional semantics for tool use, addressing issues of contention and failure in external systems.
Why It Matters
As large language model (LLM) agents increasingly interact with external systems, ensuring reliable and safe operations is critical. Atomix's approach to managing tool calls with transactional semantics can significantly improve the robustness of workflows, making it relevant for developers and researchers in AI and machine learning.
Key Takeaways
- Atomix introduces a runtime that ensures safe tool use for LLM agents.
- The system employs progress-aware transactional semantics to manage tool calls.
- Transactional retry mechanisms enhance task success rates under failure conditions.
- Frontier-gated commits improve isolation during contention and speculation.
- The approach is validated through real workloads with fault injection.
Computer Science > Machine Learning arXiv:2602.14849 (cs) [Submitted on 16 Feb 2026] Title:Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows Authors:Bardia Mohammadi, Nearchos Potamitis, Lars Klein, Akhil Arora, Laurent Bindschaedler View a PDF of the paper titled Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows, by Bardia Mohammadi and 4 other authors View PDF HTML (experimental) Abstract:LLM agents increasingly act on external systems, yet tool effects are immediate. Under failures, speculation, or contention, losing branches can leak unintended side effects with no safe rollback. We introduce Atomix, a runtime that provides progress-aware transactional semantics for agent tool calls. Atomix tags each call with an epoch, tracks per-resource frontiers, and commits only when progress predicates indicate safety; bufferable effects can be delayed, while externalized effects are tracked and compensated on abort. Across real workloads with fault injection, transactional retry improves task success, while frontier-gated commit strengthens isolation under speculation and contention. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA) Cite as: arXiv:2602.14849 [cs.LG] (or arXiv:2602.14849v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.14849 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission...