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.

Collaborative spectrum sensing enables distributed RF devices to share observations and improve classification performance in congested or contested environments. While most prior research assumes homogeneous sensors, real-world systems involve heterogeneous devices with diverse hardware, bandwidth, and channel conditions. This work introduces CLOUD-D RF, a framework that leverages convolutional neural networks at edge radios to extract learned features that are sent to a cloud-based fusion center for improved modulation classification. Phase I validated the concept with synthetic datasets, achieving over 97% classification accuracy. Phase II implemented a hardware testbed using GNU Radio with USRP radios, demonstrating real-time modulation classification over a distributed edge network of radios. Results confirm that cloud-based fusion reduces bandwidth requirements while maintaining accuracy, offering a practical path toward resilient and scalable spectrum sensing systems.