{"slug": "a-self-defining-operator-for-distributed-applications", "title": "A Self-Defining Operator for Distributed Applications", "summary": "A self-defining operator (SDO) that distills resolved incidents into reusable playbooks and telemetry-triggered detectors cut resolution time by 52% on recurring faults across 11 open-source microservice applications, according to an arXiv paper submitted 10 October 2026 by Shihang Li and revised 14 October 2026. The SDO runs detectors continuously and invokes LLMs only when a detector fires, so token cost scales with incident count rather than operational time. The system is open source at github.com/self-defining-systems/sdo.", "body_md": "# Computer Science > Distributed, Parallel, and Cluster Computing\n\n  [Submitted on 10 Oct 2026 (\n\n[v1](https://arxiv.org/abs/2610.14547v1)), last revised 14 Oct 2026 (this version, v2)]\n# Title:A Self-Defining Operator for Distributed Applications\n\n[View PDF](https://arxiv.org/pdf/2610.14547)\n\n[HTML (experimental)](https://arxiv.org/html/2610.14547v2)\n\nAbstract:To operate distributed applications with high availability, operators must rapidly diagnose and mitigate any incident. LLM-based agents can help, but unlike experienced human operators they do not improve as they handle more incidents. Each investigation starts from scratch, re-deriving likely causes and re-testing hypotheses even when a similar fault was resolved in the past. We present a self-defining operator (SDO) that improves with time by updating its operational behavior and memory. When SDO resolves an incident, it distills that experience into playbooks, which describe reusable diagnostic instructions for future incidents with similar faults, and detectors, scripts that take application telemetry as input and output relevant playbook(s). SDO continuously runs the detectors and invokes LLMs only when one fires, so its token cost scales with incident count rather than with operational time. Across 11 open-source microservice applications, SDO cuts resolution time by 52% on recurring faults while keeping per-incident cost low. SDO is open source at [this https URL](https://github.com/self-defining-systems/sdo).\n    \n\n## Submission history\n\nFrom: Shihang Li [\n[view email](https://arxiv.org/show-email/87beba35/2610.14547)]\n\n**Sat, 10 Oct 2026 04:08:13 UTC (2,590 KB)**\n\n[\\[v1\\]](https://arxiv.org/abs/2610.14547v1)\n**[v2]** Wed, 14 Oct 2026 06:00:51 UTC (2,590 KB)\n\n### Additional Features\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/a-self-defining-operator-for-distributed-applications", "canonical_source": "https://arxiv.org/abs/2610.14547", "published_at": "2026-10-24 07:46:00+00:00", "updated_at": "2026-10-24 08:12:36.176375+00:00", "lang": "en", "topics": ["ai-agents", "artificial-intelligence", "mlops", "ai-research"], "entities": ["Shihang Li", "arXiv", "self-defining operator (SDO)", "github.com/self-defining-systems/sdo"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/a-self-defining-operator-for-distributed-applications", "markdown": "https://wpnews.pro/news/a-self-defining-operator-for-distributed-applications.md", "text": "https://wpnews.pro/news/a-self-defining-operator-for-distributed-applications.txt", "jsonld": "https://wpnews.pro/news/a-self-defining-operator-for-distributed-applications.jsonld"}}