Demo: Intent-Driven Conversational Root Cause Analysis in Kubernetes Using LLMs and Dependency Graphs

Pedro R. Tomás, Tomás Dias, Marco Neto, Luís Rosa, Jorge Proença, Tiago Cruz, Luís Cordeiro

NOMS · 2026

Publisher / bibliographic record ↗ DOI ↗

Abstract

Root Cause Analysis in cloud-native environments remains challenging due to the dynamic nature of micro-services and the operational expertise required for diagnosis. While recent approaches advance multimodal Root Cause Analysis through learning-based techniques, limited attention has been given to interactive workflows and real-time operational integration. This demo presents an end-to-end RCA framework combining multimodal analysis with an intent-driven conversational interface for Kubernetes environments. The system integrates real-time log ingestion, root cause ranking based on graph correlations and anomaly signals, and LLM-assisted reasoning to support human-in-the-loop diagnosis and operational control. Through a unified dashboard, users can inspect logs, visualise environment topology, and perform pod-level RCA without requiring Kubernetes expertise or command-line knowledge. The demonstration showcases the framework on a containerised testbed featuring OWASP Juice Shop and PostgreSQL, highlighting practical Root Cause Analysis workflows enabled by conversational interaction and graph-based exploration.

Bibliographic details

Type: Conference paper
Year: 2026
Authors: Pedro R. Tomás, Tomás Dias, Marco Neto, Luís Rosa, Jorge Proença, Tiago Cruz, Luís Cordeiro