# Agentic Network Traffic Monitoring

arXiv: https://arxiv.org/abs/2609.32778 · HTML: https://mlsanity.com/paper/2609.32778/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 26 Sep 2026.

## Placed in

- LLM agents and tool use

## Builds on (most cited first, 7 of 7)

- [AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways](https://mlsanity.com/paper/2406.02630/) (2024)
- [A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents](https://mlsanity.com/paper/2506.23844/) (2025)
- [Hypersparse Neural Network Analysis of Large-Scale Internet Traffic](https://mlsanity.com/paper/1904.04396/) (2019)
- [GraphBLAS on the Edge: Anonymized High Performance Streaming of Network Traffic](https://mlsanity.com/paper/2203.13934/) (2022)
- [Hypersparse Network Flow Analysis of Packets with GraphBLAS](https://mlsanity.com/paper/2209.05725/) (2022)
- [Deployment of Real-Time Network Traffic Analysis using GraphBLAS Hypersparse Matrices and D4M Associative Arrays](https://mlsanity.com/paper/2309.02464/) (2023)
- [DBOS Network Sensing: A Web Services Approach to Collaborative Awareness](https://mlsanity.com/paper/2509.09898/) (2025)

## Abstract (arXiv)

As the use of agentic artificial intelligence increases in nearly every industry, there exists a widening attack surface. It is necessary to monitor agents to ensure that agents are acting in a way that is aligned with the users intent. Auditing an agent's network traffic provides a clear record of the agent interactions. This work presents a novel approach to monitoring the network traffic of agentic systems using complex valued hypersparse traffic matrices by integrating DBOS (DataBase OS), the OneSparse PostgreSQL database, and the GraphBLAS math library. To develop these concepts an agentic simulator was constructed, allowing a varying numbers of AI agents to collectively survey a virtual environment using different strategies. The resulting network traffic matrices enable easy monitoring of the AI agents.