mlsanityData to 14 Oct 2026

ENCO: Life-Cycle Management of Enterprise-Grade Copilots

arXiv 2412.06099 ↗First posted 8 Dec 2024cs.SE cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 57 references that are in the map. First posted on arXiv 8 Dec 2024.

Builds on

20 of 57 references.

References · 20

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
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  12. 2022Questions Are All You Need to Train a Dense Passage Retrievercited by 47
8 more
  1. 2024GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Modelcited by 40
  2. 2024Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approachcited by 40
  3. 2023D-Bot: Database Diagnosis System using Large Language Modelscited by 28
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  5. 2021Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansioncited by 20
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026GraphMind: From Operational Traces to Self-Evolving Workflow Automationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Leveraging Resolved Incident History for LLM-Assisted Software Bug Diagnosis ↗
  2. 2026Towards LLM Accelerated Rapid Reviews for Software Tool Discovery -- Case for Log Anomaly Detection
  3. 2026RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents
  4. 2026LogCopilot: Automating Log Aggregation Analysis through Large Language Models ↗
  5. 2025RAGuard: A Novel Approach for in-context Safe Retrieval Augmented Generation for LLMs
  6. 2026Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
  7. 2025From Code Generation to Software Testing: AI Copilot with Context-Based RAG
  8. 2026"Where is My Troubleshooting Procedure?": Studying the Potential of RAG in Assisting Failure Resolution of Large Cyber-Physical System ↗