ENCO: Life-Cycle Management of Enterprise-Grade Copilots
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908757843247104 |
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| author | Zhu, Yiwen Demarne, Mathieu Deng, Kai Wang, Wenjing Sahoo, Nutan Vermareddy, Divya Lerner, Hannah Lu, Yunlei Bararia, Swati Bhavan, Anjali Zhang, William Li, Xia Lin, Katherine Cilimdzic, Miso Krishnan, Subru |
| author_facet | Zhu, Yiwen Demarne, Mathieu Deng, Kai Wang, Wenjing Sahoo, Nutan Vermareddy, Divya Lerner, Hannah Lu, Yunlei Bararia, Swati Bhavan, Anjali Zhang, William Li, Xia Lin, Katherine Cilimdzic, Miso Krishnan, Subru |
| contents | Software engineers frequently grapple with the challenge of accessing disparate documentation and telemetry data, including TroubleShooting Guides (TSGs), incident reports, code repositories, and various internal tools developed by multiple stakeholders. While on-call duties are inevitable, incident resolution becomes even more daunting due to the obscurity of legacy sources and the pressures of strict time constraints. To enhance the efficiency of on-call engineers (OCEs) and streamline their daily workflows, we introduced DECO-a comprehensive framework for developing, deploying, and managing enterprise-grade copilots tailored to improve productivity in engineering routines. This paper details the design and implementation of the DECO framework, emphasizing its innovative NL2SearchQuery functionality and a lightweight agentic framework. These features support efficient and customized retrieval-augmented-generation (RAG) algorithms that not only extract relevant information from diverse sources but also select the most pertinent skills in response to user queries. This enables the addressing of complex technical questions and provides seamless, automated access to internal resources. Additionally, DECO incorporates a robust mechanism for converting unstructured incident logs into user-friendly, structured guides, effectively bridging the documentation gap. Since its launch in September 2023, ENCO has demonstrated its effectiveness through widespread adoption, enabling tens of thousands of interactions and engaging hundreds of monthly active users (MAU) across dozens of organizations within the company. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06099 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ENCO: Life-Cycle Management of Enterprise-Grade Copilots Zhu, Yiwen Demarne, Mathieu Deng, Kai Wang, Wenjing Sahoo, Nutan Vermareddy, Divya Lerner, Hannah Lu, Yunlei Bararia, Swati Bhavan, Anjali Zhang, William Li, Xia Lin, Katherine Cilimdzic, Miso Krishnan, Subru Software Engineering Artificial Intelligence Software engineers frequently grapple with the challenge of accessing disparate documentation and telemetry data, including TroubleShooting Guides (TSGs), incident reports, code repositories, and various internal tools developed by multiple stakeholders. While on-call duties are inevitable, incident resolution becomes even more daunting due to the obscurity of legacy sources and the pressures of strict time constraints. To enhance the efficiency of on-call engineers (OCEs) and streamline their daily workflows, we introduced DECO-a comprehensive framework for developing, deploying, and managing enterprise-grade copilots tailored to improve productivity in engineering routines. This paper details the design and implementation of the DECO framework, emphasizing its innovative NL2SearchQuery functionality and a lightweight agentic framework. These features support efficient and customized retrieval-augmented-generation (RAG) algorithms that not only extract relevant information from diverse sources but also select the most pertinent skills in response to user queries. This enables the addressing of complex technical questions and provides seamless, automated access to internal resources. Additionally, DECO incorporates a robust mechanism for converting unstructured incident logs into user-friendly, structured guides, effectively bridging the documentation gap. Since its launch in September 2023, ENCO has demonstrated its effectiveness through widespread adoption, enabling tens of thousands of interactions and engaging hundreds of monthly active users (MAU) across dozens of organizations within the company. |
| title | ENCO: Life-Cycle Management of Enterprise-Grade Copilots |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2412.06099 |