arXiv:2607. 08010v1 Announce Type: cross Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request.
By Kalle Kujanp\"a\"a, Ning Liu, Shahnawaz Alam, Yeshwanth Reddy Sura, Tianyu Yang, Kristina Klinkner, Shervin Malmasi
The paper introduces FlowAgent, an AI agent deployed at Google to automatically repair test failures in the pre-submit continuous integration workflow. FlowAgent uses a ReAct-style generate-and-validate loop with strict latency and quality filters, and was evaluated on 195 real-world failures with a 67.18% accuracy rate. After deployment, it suggested fixes on 295,508 changes, with developers previewing 65,069 and applying 28,554, and received positive feedback from interviews.
By Celal Ziftci, Spencer Greene, Ray Liu, Livio Dalloro, Lorenzo Dini
arXiv:2607. 16345v1 Announce Type: cross Abstract: Modern agentic systems increasingly rely on skills: installable packages of natural language and code that teach an LLM agent to perform a domain task.
By Tejas Singh Anand, Yuet Ying Christina Wang, Wanting Jiang, Steve Masson, Tian Zheng, Bingjie Zhou
Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment.
The paper introduces rebuild‑dossier, an open‑source tool that locks an application’s real interface before code is written and enforces one‑test‑at‑a‑time building through automated checks. In experiments, a compliant agent failed a held‑back test while a rule‑breaking agent passed, showing that a passing test suite can be gamed. The study also demonstrates that the automated check mechanism, rather than interface‑locking alone, is crucial for reliable rebuilds, and that multi‑level verification catches errors that single‑level checks miss.
By Parker Fawcett
arXiv:2607. 18847v1 Announce Type: cross Abstract: Agentic systems integrate LLM driven planning with interfaces to external tools, making data leakage and tool misuse feasible via instruction/data boundary failures and prompt injection attacks.
By Akansha Shukla, Emily Bellov, Parth Atulbhai Gandhi, Yuval Elovici, Asaf Shabtai