We present a conversational AI framework for monitoring and analysis of distributed AI/ML infrastructure based on a multi-agent architecture. The system incorporates specialized agents for topology discovery, health assessment, network flow analysis, and root cause analysis (RCA), accessible through a natural language interface. An agent orchestration component parses and routes user queries, allowing the platform to support a range of cluster observability and troubleshooting tasks using both sequential and parallel agent workflows. Each agent interacts with dedicated analytical microservices via Model Context Protocol (MCP), enabling modular and extensible evaluation of infrastructure state. We detail the system architecture, agent design, setup used for experiments and discuss the implications of conversational AI agents for automated RCA and operational efficiency in monitoring and troubleshooting large-scale ML infrastructure.