8 papers
The Potential of Erroneous Outbound Traffic Analysis to Unveil Silent Internal Anomalies
Andrea Sordello, Zhihao Wang, Kai Huang +2
Passive measurement has traditionally focused on inbound traffic to detect malicious activity, based on the assumption that threats originate externally. In this paper, we offer a…
MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability
Tie Ma, Yixi Chen, Vaastav Anand +8
We present MAESTRO, an evaluation suite for the testing, reliability, and observability of LLM-based MAS. MAESTRO standardizes MAS configuration and execution through a unified int…
A Network Arena for Benchmarking AI Agents on Network Troubleshooting
Zhihao Wang, Alessandro Cornacchia, Alessio Sacco +3
Agentic systems, powered by Large Language Models (LLMs), assist network engineers with network configuration synthesis and network troubleshooting tasks. For network troubleshooti…
DMAS-Forge: A Framework for Transparent Deployment of AI Applications as Distributed Systems
Alessandro Cornacchia, Vaastav Anand, Muhammad Bilal +2
Agentic AI applications increasingly rely on multiple agents with distinct roles, specialized tools, and access to memory layers to solve complex tasks -- closely resembling servic…
ChamaleoNet: Programmable Passive Probe for Enhanced Visibility on Erroneous Traffic
Zhihao Wang, Alessandro Cornacchia, Andrea Bianco +4
Traffic visibility remains a key component for management and security operations. Observing erroneous traffic, i.e., unanswered requests or error messages, is fundamental to detec…
Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting
Zhihao Wang, Alessandro Cornacchia, Franco Galante +3
Recent research has demonstrated the effectiveness of Artificial Intelligence (AI), and more specifically, Large Language Models (LLMs), in supporting network configuration synthes…