Optimizing Operational Efficiency in Platform Teams with AI-driven Chatbots : Design and Evaluation of an Intelligent Assistant for DevOps Operations

Sammanfattning

Platform engineering teams in large enterprises face growing operational challenges due to the increasing scale and complexity of cloud-native systems. A significant portion of their workload consists of repetitive workflows, such as repeatedly searching logs, consulting documentation, and updating incident tickets. These tasks consume valuable engineering time, fragment knowledge across multiple systems, and delay efficient problem resolution. This thesis addresses this problem by designing and implementing an AI-driven chatbot that automates repetitive operational workflows through a conversational interface. The system is built on a LangGraph-based orchestration framework that coordinates a master-agent workflow, routing queries to specialized tools: Elasticsearch for log retrieval, Qdrant as a vector database for semantic documentation search, and ServiceNow for incident management. A continuous embedding pipeline, based on HuggingFace models, ensures that the knowledge base is regularly updated with documentation, logs, and historical incident data. The contribution of this work lies in providing a unified, context-aware interface for platform engineers that not only retrieves information but also executes repetitive workflows automatically. By integrating knowledge management with workflow automation, the system reduces the need for manual effort in operational tasks and supports more efficient platform operations. The solution is evaluated through deployment in an enterprise case study at Volvo Cars, demonstrating its potential to improve workflow efficiency, reduce repetitive work, and enhance the overall effectiveness of platform engineering teams.