DECO: Life-Cycle Management of Enterprise-Grade Copilots (2412.06099v2)

Abstract: 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, DECO 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.

• The paper introduces DECO, a framework that manages the entire chatbot lifecycle and streamlines incident documentation for software engineers.
• It employs retrieval-augmented-generation techniques and fine-tuned hierarchical planners to ensure precise and actionable information retrieval.
• The framework reduces incident triaging time by 10–20 minutes per incident, significantly boosting productivity in large-scale enterprise environments.

Review of "DECO: Life-Cycle Management of Enterprise-Grade Chatbots"

The paper presents DECO, a framework engineered to facilitate the lifecycle management of enterprise-grade chatbots tailored specifically for enhancing the productivity of software engineers. Developed by a team from Microsoft, DECO addresses several persistent challenges faced by engineers during their routine tasks, particularly those associated with on-call duties, by providing a streamlined, automated system to manage complex information retrieval needs. The system integrates significant recent advancements in LLMs to mitigate the issue of manual information retrieval from disparate resources, including Troubleshooting Guides (TSGs), incident reports, telemetry data, and more.

Core Contributions

DECO's primary contributions are organized around the following advancements:

1. End-to-End Management of Chatbots: DECO is structured to handle the entire lifecycle of chatbots from development and deployment to management, supporting the daily routines and incident resolution of engineers with efficiency.
2. Improvement in Incident Documentation: By converting unstructured incident logs into structured guides, DECO effectively addresses documentation gaps, thus utilizing incident databases to create comprehensive and ready resources for engineers.
3. Optimization of Retrieval Algorithms: The introduction of fine-tuned retrieval and hierarchical planners ensures precise document, toolkit, and code segment retrievals, significantly enhancing response accuracy.
4. Robust Evaluation System: The system incorporates an extensive evaluation and monitoring infrastructure that facilitates continuous improvements, grounded in real-world usage feedback and detailed performance metrics.

Methodological Insights

DECO employs innovative retrieval-augmented-generation (RAG) techniques powered by advanced LLMs, which allow for efficient information extraction from numerous internal sources. The NL2SearchQuery and hierarchical planners form the bedrock for these operations by transforming complex natural language queries into actionable search directives. This leads to the accurate and contextually relevant retrieval of data, enabling the integration of additional knowledge sources and the seamless operation of internal toolkits.

DECO's architecture, comprising data preprocessing, backend services, frontend services, and evaluation capabilities, offers a structured approach to leveraging mature Azure solutions for large-scale enterprise deployment. The emphasis on Azure AI Search for indexing and retrieval allows for a streamlined pipeline with enhanced retrieval speeds and decreased latency. Evaluative measures ensure real-time monitoring and ongoing performance refinement.

Numerical Results and Practical Implications

A notable quantitative highlight from DECO's deployment within Microsoft is the substantial reduction of time required for incident triaging—estimated at 10 to 20 minutes per incident—which translates into significant annual cost savings in terms of operational efficiency. The system has seen broad acceptance and use, as evidenced by tens of thousands of interactions with hundreds of active users, highlighting its impact on organizational productivity.

Implications and Future Directions

The theoretical underpinnings and practical implementation of DECO have multiple implications for the ongoing development of AI-driven enterprise tools. By bridging gaps between varied datasets and providing automated, accurate responses, DECO stands as a model of how NLP capabilities can be harnessed to foster innovation within the field of software engineering.

Looking ahead, the potential enhancements to DECO could involve refining document retrieval strategies through the analysis of user feedback, enhancing the memory management system for handling extensive chat histories, and optimizing the personalization features to cater to unique engineer preferences. These developments may drive further improvements in the efficiency and adaptability of enterprise chatbots.

In conclusion, the DECO framework exemplifies a significant advancement in the life-cycle management of chatbots tailored to the enterprise environment, providing meaningful enhancements to the workflows of software engineers. Through its robust architecture and continuous improvement pipeline, DECO offers a compelling blueprint for the development of future AI-powered solutions in complex operational contexts.