@misc{zhu2024enco,
author = {Zhu, Yiwen and Demarne, Mathieu and Deng, Kai and Wang, Wenjing and Sahoo, Nutan and Lerner, Hannah and Bhavan, Anjali and Vermareddy, Divya and Lu, Yunlei and Bararia, Swati and Zhang, William and Li, Xia and Lin, Katherine and Cilimdzic, Miso and Krishnan, Subru},
title = {ENCO: Deploying Production-Scale Engineering Copilots},
howpublished = {arXiv},
year = {2024},
month = {December},
abstract = {Software engineers frequently grapple with the challenge of accessing fragmented documentation and telemetry data, such as Troubleshooting Guides (TSGs), incident reports, code repositories, and internal tools maintained by different teams. In this work, we introduced ENCO, a comprehensive framework for developing, deploying, and managing copilots tailored to improve productivity in large scale production scenarios. The framework combines an innovative NL2SearchQuery module with a lightweight hierarchical agentic planner to enable accurate and efficient retrieval-augmented generation (RAG) for code, semi-structured data and documents. These components allow the copilot to retrieve relevant information from diverse sources and invoke the right skills with low latency to answer highly complex technical questions. Since its launch in September 2023, ENCO has demonstrated its effectiveness through widespread adoption, enabling tens of thousands of interactions and engaging over 1,000 monthly active users (MAUs). The system has been continuously optimized based on usage patterns and user feedback, resulting in measurable improvements in response relevance, latency, and user satisfaction.},
url = {http://approjects.co.za/?big=en-us/research/publication/enco-deploying-production-scale-engineering-copilots/},
}