# Towards an Agentic Workflow for Internet Measurement Research

arXiv: https://arxiv.org/abs/2511.10611 · HTML: https://mlsanity.com/paper/2511.10611/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 13 Nov 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 4 of 4)

- [Designing Network Algorithms via Large Language Models](https://mlsanity.com/paper/2404.01617/) (2024)
- [Nautilus: A Framework for Cross-Layer Cartography of Submarine Cables and IP Links](https://mlsanity.com/paper/2302.14201/) (2023)
- [Multilevel MDA-Lite Paris Traceroute](https://mlsanity.com/paper/1809.10070/) (2018)
- [Xaminer: An Internet Cross-Layer Resilience Analysis Tool](https://mlsanity.com/paper/2401.08073/) (2024)

## Built on by (most cited first, 2 of 2)

- [Can AI Agents Detect and Repair Artifact Drift in Network Experiments?](https://mlsanity.com/paper/2609.09849/) (2026)
- [Can You Check That? The Checkability Boundary for Local LLM Network Automation](https://mlsanity.com/paper/2609.31540/) (2026)

## Abstract (arXiv)

Internet measurement research faces an accessibility crisis: complex analyses require custom integration of multiple specialized tools that demands specialized domain expertise. When network disruptions occur, operators need rapid diagnostic workflows spanning infrastructure mapping, routing analysis, and dependency modeling. However, developing these workflows requires specialized knowledge and significant manual effort. We present ArachNet, the first system demonstrating that LLM agents can independently generate measurement workflows that mimics expert reasoning. Our core insight is that measurement expertise follows predictable compositional patterns that can be systematically automated. ArachNet operates through four specialized agents that mirror expert workflow, from problem decomposition to solution implementation. We validate ArachNet with progressively challenging Internet resilience scenarios. The system independently generates workflows that match expert-level reasoning and produce analytical outputs similar to specialist solutions. Generated workflows handle complex multi-framework integration that traditionally requires days of manual coordination. ArachNet lowers barriers to measurement workflow composition by automating the systematic reasoning process that experts use, enabling broader access to sophisticated measurement capabilities while maintaining the technical rigor required for research-quality analysis.