Airavat: An Agentic Framework for Internet Measurement

2026-02-24

AI Summary

This work addresses the methodological fragility and limited verifiability inherent in complex Internet measurement analyses, which traditionally rely on manual orchestration by experts. To overcome these limitations, the authors propose the first multi-agent framework tailored for Internet measurement, capable of collaboratively generating verifiable measurement workflows. The framework encodes five decades of domain knowledge into a reasoning-enabled knowledge graph and integrates a methodology validation engine with a tool registry to automatically recommend and verify technical approaches. Evaluated across four case studies, the system autonomously produces workflows comparable to those crafted by experts, makes sound architectural decisions, effectively tackles novel problems lacking ground truth, and uncovers methodological flaws undetectable by conventional testing practices.

Technology Category

• Multiagent Systems: Mechanism Design
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Web Mining and Content Analysis: Web measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Internet measurement faces twin challenges: complex analyses require expert-level orchestration of tools, yet even syntactically correct implementations can have methodological flaws and can be difficult to verify. Democratizing measurement capabilities thus demands automating both workflow generation and verification against methodological standards established through decades of research. We present Airavat, the first agentic framework for Internet measurement workflow generation with systematic verification and validation. Airavat coordinates a set of agents mirroring expert reasoning: three agents handle problem decomposition, solution design, and code implementation, with assistance from a registry of existing tools. Two specialized engines ensure methodological correctness: a Verification Engine evaluates workflows against a knowledge graph encoding five decades of measurement research, while a Validation Engine identifies appropriate validation techniques grounded in established methodologies. Through four Internet measurement case studies, we demonstrate that Airavat (i) generates workflows matching expert-level solutions, (ii) makes sound architectural decisions, (iii) addresses novel problems without ground truth, and (iv) identifies methodological flaws missed by standard execution-based testing.

Problem

• Internet measurement
• methodological flaws
• workflow verification
• democratizing measurement
• expert-level orchestration

Innovation

• agentic framework
• Internet measurement
• workflow generation
• methodological verification
• knowledge graph