[Paper] Airavat: An Agentic Framework for Internet Measurement

Source: arXiv - 2602.20924v1

Overview

The paper introduces Airavat, a novel “agentic” framework that automates the creation and verification of Internet‑measurement workflows. By mimicking the reasoning steps of seasoned researchers, Airavat can generate end‑to‑end measurement pipelines that are both technically sound and methodologically rigorous—something that traditionally required deep domain expertise.

Key Contributions

• Agentic workflow generation – Three coordinated agents (decomposer, designer, coder) automatically translate a high‑level measurement goal into a concrete, executable pipeline.
• Methodological verification engine – Checks generated workflows against a knowledge graph that encodes five decades of measurement best practices, flagging violations of established standards.
• Validation engine – Suggests and integrates appropriate validation techniques (e.g., ground‑truth comparison, statistical sanity checks) based on the specific measurement task.
• Tool registry integration – Dynamically selects from a curated library of existing measurement utilities (e.g., traceroute, ping, BGP collectors) to avoid reinventing the wheel.
• Empirical validation – Four diverse Internet‑measurement case studies demonstrate that Airavat matches expert‑crafted solutions, discovers architectural flaws, and handles novel problems lacking ground truth.

Methodology

1. Problem Decomposition Agent – Takes a natural‑language measurement objective (e.g., “measure latency variance across CDN edge nodes”) and breaks it into sub‑tasks (data collection, preprocessing, analysis).
2. Solution Design Agent – Consults the Tool Registry and the Methodology Knowledge Graph to pick suitable instruments and design a pipeline architecture (parallel probes, sampling rates, storage format).
3. Implementation Agent – Generates the actual code (scripts, Dockerfiles, CI pipelines) that stitches the chosen tools together, adhering to the design blueprint.
4. Verification Engine – Runs static and semantic checks: it ensures that the chosen metrics, sampling strategies, and statistical tests align with the rules stored in the knowledge graph (e.g., “latency studies must include clock‑synchronization validation”).
5. Validation Engine – Automatically attaches validation steps such as cross‑checking with known reference datasets, bootstrapping confidence intervals, or performing controlled experiments.

All agents communicate through a lightweight orchestration layer, allowing developers to intervene, override decisions, or inject custom modules without breaking the verification guarantees.

Results & Findings

• Expert‑level parity – In three of the four case studies, the generated workflows produced the same quantitative insights as those crafted by senior network researchers.
• Architectural soundness – Airavat identified sub‑optimal probe placement and missing redundancy in two expert pipelines, leading to more robust data collection after minor adjustments.
• Novel problem handling – For a measurement of emerging QUIC‑based traffic where no ground truth existed, Airavat proposed a hybrid validation strategy (synthetic traffic injection + statistical anomaly detection) that proved effective.
• Methodological flaw detection – The verification engine caught a common mistake—using a single‑host ping as a proxy for path latency—that standard unit tests missed, prompting a redesign of the experiment.

Overall, Airavat reduced the time to produce a vetted measurement pipeline from weeks (manual) to hours (automated).

Practical Implications

• Lower the entry barrier – Network operators, security teams, and cloud engineers can now spin up reliable measurement studies without hiring a specialist researcher.
• Continuous measurement as code – Airavat’s generated pipelines integrate naturally with CI/CD systems, enabling automated, repeatable Internet‑health checks (e.g., BGP hijack detection, CDN performance monitoring).
• Compliance and auditability – The verification logs provide a traceable record of methodological adherence, useful for regulatory reporting or internal governance.
• Rapid prototyping of new metrics – When a novel protocol or service emerges, developers can quickly prototype measurement experiments, relying on the validation engine to suggest sound evaluation techniques.
• Tool ecosystem unification – By centralizing tool selection, Airavat helps organizations avoid tool sprawl and ensures that the latest, community‑vetted utilities are used.

Limitations & Future Work

• Knowledge graph coverage – The verification engine is only as good as the curated rules; niche or emerging measurement domains may lack representation, leading to false positives or missed issues.
• Scalability of agent coordination – While the current prototype handles modest‑scale studies, scaling to massive, distributed measurement campaigns (e.g., millions of probes) will require more efficient orchestration and resource management.
• Human‑in‑the‑loop refinement – The system currently assumes that the high‑level goal is well‑specified; ambiguous objectives can cause the decomposition agent to generate sub‑optimal task breakdowns. Future work aims to incorporate interactive clarification dialogs.
• Extending validation techniques – Adding support for machine‑learning‑based anomaly detection and causal inference methods would broaden the framework’s applicability to more complex Internet‑behavior studies.

Airavat marks a significant step toward democratizing rigorous Internet measurement, turning what was once a niche academic exercise into an accessible, automated capability for the broader tech community.

Authors

• Alagappan Ramanathan
• Eunju Kang
• Dongsu Han
• Sangeetha Abdu Jyothi

Paper Information

• arXiv ID: 2602.20924v1
• Categories: cs.NI, cs.AI, cs.SE
• Published: February 24, 2026
• PDF: Download PDF