Automating Internet Research with Intelligent Agents

by

Author: Denis Avetisyan

A new system harnesses the power of artificial intelligence to build and execute complex network measurement studies, lowering the barriers to in-depth internet analysis.

ArachNet employs a distributed agent architecture—comprising QueryMind for problem decomposition, WorkflowScout for solution pathway design, SolutionWeaver for executable implementation, and RegistryCurator for continuous capability evolution—to dynamically address complex challenges.
ArachNet employs a distributed agent architecture—comprising QueryMind for problem decomposition, WorkflowScout for solution pathway design, SolutionWeaver for executable implementation, and RegistryCurator for continuous capability evolution—to dynamically address complex challenges.

This paper introduces ArachNet, an agentic system leveraging large language models to automate the composition of Internet measurement workflows for improved network resilience and automated analysis.

Despite growing demands for rapid network diagnostics, Internet measurement research remains hampered by complex workflows requiring specialized expertise and manual integration of disparate tools. This paper, ‘Towards an Agentic Workflow for Internet Measurement Research’, introduces ArachNet, a novel system demonstrating that large language model (LLM) agents can autonomously compose measurement workflows mirroring expert reasoning. ArachNet achieves this by systematically automating compositional patterns inherent in measurement expertise, effectively lowering the barrier to sophisticated network analysis. Could agentic systems like ArachNet fundamentally reshape how we approach Internet resilience research and broaden access to critical measurement capabilities?

The Fragility of Connection: Understanding Internet Resilience

Despite its reputation for unwavering connectivity, the Internet faces a growing spectrum of threats to its operational stability. Submarine cable failures, often caused by natural disasters or even ship anchors, represent a significant single point of failure, while cascading failures – where the failure of one network component triggers a chain reaction – pose a more systemic risk. These disruptions aren’t simply inconveniences; they can have substantial economic and social consequences, impacting everything from financial markets to emergency services. Consequently, a shift from reactive troubleshooting to proactive resilience analysis is critical. This necessitates sophisticated methods for identifying vulnerabilities, predicting potential failure scenarios, and developing strategies to mitigate their impact – ensuring the continued flow of information even under duress.

Historically, addressing disruptions to internet connectivity has relied heavily on manual diagnostics and reactive troubleshooting – a process increasingly inadequate for the modern network. These conventional methods, while once sufficient, struggle to keep pace with the sheer volume of data and the intricate interdependencies within today’s global infrastructure. When a submarine cable fails or a cascading failure begins, human operators must sift through vast amounts of telemetry, identify the root cause, and implement a solution – a time-consuming process that exacerbates outages and impacts countless users. The exponential growth of network scale and complexity has simply outstripped the capacity of manual intervention, demanding a paradigm shift towards automated systems capable of proactively identifying and mitigating threats before they escalate into widespread disruptions.

Truly understanding Internet resilience demands a departure from passively observing network performance and a move towards dynamically analyzing behavior under deliberately applied stress. Current monitoring systems largely report on what has happened, offering limited insight into potential vulnerabilities or the propagation of failures. Instead, effective analysis necessitates automated systems capable of simulating disruptions – such as link failures or increased traffic loads – and rapidly assessing the network’s response. This proactive approach allows researchers and operators to identify critical points of failure, evaluate the effectiveness of mitigation strategies, and ultimately, build a more robust and adaptable infrastructure capable of withstanding increasingly complex threats. Such automated systems aren’t simply measuring connectivity; they are actively probing the network’s ability to self-heal and maintain functionality in the face of adversity.

The escalating complexity of the internet demands a fundamental change in how network resilience is approached, necessitating a move towards automated, agent-driven workflows. These systems utilize software agents that proactively assess network health, predict potential disruptions – such as those stemming from physical damage or cyberattacks – and dynamically reconfigure traffic routing to maintain connectivity. Rather than relying on manual intervention after a failure occurs, these agents continuously monitor, analyze, and respond to anomalies in real-time, offering a significant improvement in response time and scalability. This paradigm shift allows networks to not only recover from disruptions but also to anticipate and mitigate them before they impact users, ultimately ensuring a more stable and reliable internet experience. The future of internet resilience hinges on embracing this level of automation and intelligent response.

ArachNet: Orchestrating Resilience Through Agentic Automation

ArachNet utilizes an agentic system to automate Internet measurement workflows by modeling the reasoning processes of human experts, differing from traditional approaches that rely on pre-defined scripts. This involves capturing the logic behind how measurements are composed to achieve specific goals, rather than simply automating a sequence of commands. The system doesn’t just execute tasks; it understands the intent behind them, enabling dynamic workflow creation based on desired outcomes. This approach allows ArachNet to adapt to changing network conditions and measurement requirements without manual intervention, effectively replicating the problem-solving skills of a network measurement specialist.

The Measurement Capability Registry is a core component of ArachNet, functioning as a knowledge base that details the functionalities of available Internet measurement tools. This registry doesn’t simply list tools; it explicitly defines what each tool can achieve – for example, identifying network latency, detecting routing anomalies, or mapping network topology. Each entry includes a standardized description of inputs, expected outputs, and any associated limitations. This structured metadata allows ArachNet’s agents to reason about tool capabilities, enabling the system to intelligently compose workflows based on high-level desired outcomes rather than requiring pre-defined scripts or manual configuration. The registry supports heterogeneous tools, including Traceroute processors, BGP analyzers, and topology mappers, and facilitates the automated selection and integration of these tools to achieve complex measurement goals.

ArachNet utilizes two primary agents for automated workflow composition: QueryMind and WorkflowScout. QueryMind functions as a Natural Language Processing (NLP) module, accepting user queries expressed in plain language and decomposing them into a series of discrete, actionable sub-problems. This decomposition identifies the specific measurement tasks required to address the initial query. Simultaneously, WorkflowScout consults the Measurement Capability Registry to identify available tools and capabilities that can satisfy each sub-problem. WorkflowScout then constructs a solution architecture by linking these capabilities together in a logical sequence, effectively designing a workflow to address the user’s original request. This agentic approach allows ArachNet to dynamically build measurement workflows based on semantic understanding rather than pre-defined scripts.

SolutionWeaver is the component of ArachNet responsible for converting abstract workflow architectures into executable code that leverages diverse Internet measurement tools. This includes integration with Traceroute Processors, BGP Analyzers, and Topology Mappers, among others. Critically, ArachNet achieves performance comparable to workflows designed by human experts, but with a significantly reduced codebase; the entire system operates with approximately 250 lines of code, demonstrating a high degree of automation and efficiency in workflow composition and execution.

Cross-Layer Resilience: Mapping Interdependencies and Predicting Failure

Xaminer facilitates cross-layer resilience analysis by shifting from traditional, siloed monitoring of individual network components to a comprehensive assessment of interdependencies. This framework correlates data across multiple layers – including physical, data link, network, transport, and application – to model the propagation of failures. Instead of reacting to isolated incidents, Xaminer allows operators to simulate disruptions and evaluate the impact on service delivery across the entire infrastructure. This proactive approach enables the identification of cascading failures and the development of mitigation strategies that address systemic vulnerabilities, rather than solely focusing on individual component health.

Xaminer utilizes Nautilus to construct a detailed map of network infrastructure, encompassing devices, connections, and configurations. This process involves automated discovery and analysis of network elements, establishing a dependency graph that illustrates how components relate to one another. Nautilus identifies critical network elements by analyzing traffic patterns and configuration data, pinpointing devices essential for service delivery. Furthermore, the system highlights potential single points of failure – components whose failure would result in widespread disruption – by evaluating redundancy and failover mechanisms within the discovered dependencies. The resulting infrastructure map provides a foundational understanding of network topology and interdependencies, enabling proactive resilience analysis within Xaminer.

Xaminer’s proactive vulnerability identification combines discovered infrastructure dependencies – mapped by Nautilus – with simulated impact analysis. This process determines how failures in specific network elements propagate and affect critical services. By modeling potential disruptions before they occur, Xaminer quantifies the blast radius of individual component failures and highlights single points of failure within the network topology. The resulting analysis provides actionable intelligence, enabling network operators to prioritize remediation efforts based on the severity and likelihood of potential outages, rather than relying on reactive troubleshooting after an incident.

Targeted mitigation strategies, derived from cross-layer analysis, improve network resilience by focusing resources on the most critical vulnerabilities and dependencies. Rather than applying blanket solutions, this approach allows administrators to prioritize remediation efforts based on a quantified understanding of potential impact. Specifically, identified single points of failure and critical paths, as determined through infrastructure mapping with tools like Nautilus, become the focus of redundancy implementation or proactive monitoring. This precision minimizes operational expenditure and reduces the overall attack surface, leading to a demonstrably more robust and adaptable network infrastructure capable of withstanding disruptions and maintaining service availability.

Standardizing Agent Communication: Scaling Resilience Through Interoperability

ArachNet addresses the critical need for interoperability between autonomous agents through the implementation of two novel communication protocols: Agent-to-Agent Protocol (A2A) and Model Context Protocol (MCP). A2A establishes a standardized interface, enabling diverse agents – regardless of their underlying architecture or function – to interact and exchange information effectively. Complementing this is MCP, which structures the contextual data accompanying measurements and observations, ensuring that information is not only transmitted but also accurately interpreted by receiving agents. This focus on contextualization is vital, as it minimizes ambiguity and enhances the reliability of agent-driven decision-making. By prioritizing seamless communication and data clarity, ArachNet lays the foundation for complex, coordinated agentic systems capable of tackling multifaceted challenges.

ArachNet addresses the critical need for consistent communication between autonomous agents through two core protocols: Agent-to-Agent (A2A) and Model Context Protocol (MCP). A2A establishes a uniform interface, allowing disparate agents to interact and exchange information without requiring bespoke integration for each pairing; this simplifies the architecture and reduces developmental overhead. Complementing this, MCP focuses on enriching data transmission with comprehensive contextual information surrounding measurements – details like timestamp, sensor calibration, environmental conditions, and uncertainty estimates. By structuring this context, MCP not only enhances the accuracy and reliability of data interpretation but also allows agents to intelligently assess the validity and relevance of information received, ultimately leading to more informed decision-making and robust system performance.

ArachNet significantly extends its functional scope through the incorporation of Large Language Model (LLM) Agents, effectively harnessing the advanced capabilities of these models to automate intricate processes. These LLM Agents aren’t simply added as components; they are integrated to perform complex task automation, moving beyond simple data retrieval or scripted responses. The system leverages the LLM’s capacity for natural language understanding and reasoning to dynamically adapt to changing conditions and solve problems requiring nuanced judgment. This integration allows ArachNet to tackle challenges previously demanding significant human intervention, offering a pathway to increased efficiency and scalability in agent-based workflows and demonstrating a substantial leap in autonomous system capabilities.

ArachNet’s architecture achieves notable scalability through standardized communication, dramatically reducing the complexity of coordinating multiple autonomous agents. The system demonstrates this capability by automating multi-framework orchestration – a task historically demanding days of manual coding – with a remarkably concise codebase of approximately 525 lines. This efficiency extends to sophisticated forensic analysis, where ArachNet replicates expert-level performance using roughly 750 lines of code, effectively automating a process previously reliant on extensive human effort. By streamlining agent interactions and providing a structured approach to data exchange, ArachNet unlocks the potential for complex Agentic Workflows and significantly accelerates automation initiatives across diverse applications.

The presented ArachNet system embodies a purposeful simplification of intricate processes. It distills the complexities of Internet measurement – traditionally demanding expert knowledge for workflow composition – into an automated framework. This echoes a sentiment articulated by Vinton Cerf: “The Internet is for everyone, and we need to make sure that it remains open.” ArachNet actively lowers the barrier to entry for researchers, aligning with Cerf’s vision of universal access and participation. By automating analysis and composition, the system doesn’t merely replicate expert capability; it democratizes it, ensuring broader contribution to understanding network resilience and fostering innovation.

What Lies Ahead?

The automation of Internet measurement, as demonstrated by ArachNet, does not resolve the fundamental problem of signal versus noise. It merely shifts the burden. The system efficiently composes workflows, but the validity of those workflows—the relevance of the chosen measurements to the questions asked—remains stubbornly dependent on the initial prompt. A perfect agent, then, would not simply execute, but question the premise. This demands a move beyond mere language modeling towards genuine reasoning about network behavior.

Current limitations lie not in the tooling, but in the paucity of truly representative datasets for training. The Internet is not a static entity. ArachNet, like any system trained on past data, will struggle with novel disruptions, the “black swans” of network failures. Future work must prioritize the creation of synthetic, yet realistic, failure scenarios, forcing agentic systems to generalize beyond observed patterns.

Ultimately, the goal should not be to replicate the expert, but to transcend them. To build a system capable of discovering previously unknown vulnerabilities and emergent behaviors. This demands a willingness to embrace uncertainty, to design agents that are comfortable admitting “I do not know,” and that prioritize exploration over exploitation. The disappearance of the author, in this context, is not merely a matter of code elegance, but of intellectual humility.

Original article: https://arxiv.org/pdf/2511.10611.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-15 18:31