AI Agents Take the Network to the Testbed

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Author: Denis Avetisyan

A new approach combines the speed of simulation with the realism of emulation, powered by AI coding agents, to unlock more flexible and performant network experimentation.

AgenticNet emerged from an experiment in artificial intelligence, designed not by human programmers, but by an AI coding agent itself-a system built to reverse-engineer its own creation.
AgenticNet emerged from an experiment in artificial intelligence, designed not by human programmers, but by an AI coding agent itself-a system built to reverse-engineer its own creation.

AgenticNet leverages AI-driven coding agents to create hybrid network experiments combining simulation and emulation for improved flexibility and performance.

Traditional network experimentation often necessitates choosing between the fidelity of emulation and the scalability of simulation, creating a fundamental trade-off for researchers. This paper introduces AgenticNet: Utilizing AI Coding Agents To Create Hybrid Network Experiments, a novel tool leveraging large language model-based agents to seamlessly integrate these approaches. AgenticNet facilitates hybrid network experimentation, offering increased flexibility and performance through AI-driven code generation for both simulation and emulation environments-demonstrated with Python and C++ implementations. Could this paradigm shift in network experimentation accelerate innovation and enable more realistic, large-scale testing of network protocols and applications?

Breaking the Simulation: The Limits of Network Experimentation

Network experimentation has historically faced a fundamental trade-off: reliance on either dedicated physical infrastructure or purely simulated environments. Physical laboratories, while offering a degree of realism, are burdened by substantial costs, limited scalability, and an inherent inflexibility that hinders rapid iteration and testing of novel network designs. Conversely, isolated simulations, though cost-effective and easily manipulated, often struggle to accurately replicate the complexities of real-world networks, necessitating simplifying assumptions that can compromise the validity of results. This disconnect between controlled experimentation and practical deployment poses a significant challenge for researchers and developers striving to build resilient and high-performing network protocols and security systems, as behaviors observed in simulation may not translate directly to live network conditions.

Network researchers historically faced a trade-off when testing innovations: physical laboratories, while offering a degree of realism, demand substantial financial investment in hardware and present logistical hurdles in scaling experiments or replicating complex scenarios. Conversely, relying solely on simulation environments, though cost-effective and readily scalable, often introduces inaccuracies stemming from simplified models of network behavior and traffic patterns. These abstractions can fail to capture the nuanced interactions of real-world networks, particularly under stress from events like a [latex]DDoS[/latex] attack or unexpected congestion. Consequently, protocols and security mechanisms validated in simulation may perform poorly-or even fail-when deployed in a live network, highlighting the need for experimentation approaches that bridge this gap between cost, scale, and fidelity.

The inability to accurately replicate real-world network conditions poses a substantial challenge to building truly resilient systems. Traditional validation methods struggle to predict performance under genuine stress, leaving protocols and security measures vulnerable to unforeseen exploits. Specifically, attacks like a Flood Attack, which overwhelm network resources with spurious traffic, expose this fragility; simulations often underestimate the cascading effects of congestion and packet loss, while physical labs cannot easily scale to the magnitude of a large-scale distributed denial-of-service. Consequently, defenses developed and tested in isolation may prove ineffective when confronted with the complexities of a live network, necessitating more realistic and adaptable experimentation approaches to ensure robust and reliable network infrastructure.

AgenticNet: A Hybrid Reality for Network Validation

AgenticNet represents a departure from traditional network testing methodologies by integrating both network simulation and emulation within a unified framework. Network simulation, while offering scalability and the ability to model large, complex topologies, often lacks the fidelity of real-world hardware interactions. Conversely, network emulation, utilizing physical devices, provides high realism but is constrained by scalability and cost. AgenticNet addresses these limitations by combining both approaches; a simulated network environment models the broader network, while critical components or specific functionalities are implemented and tested within a physically emulated domain. This hybrid approach allows for comprehensive testing that balances the need for both scalability and realism, enabling validation of network behavior under diverse and potentially complex conditions.

AgenticNet’s Hybrid Mode functions by integrating a Simulation Domain and an Emulation Domain, each serving distinct purposes and connected by a Bridge Link. The Simulation Domain, implemented using a network simulator, provides scalability to test large and complex network topologies, allowing for the modeling of numerous nodes and traffic patterns. Conversely, the Emulation Domain utilizes real network hardware and operating systems to achieve a high degree of realism in packet processing and protocol implementation. The Bridge Link facilitates communication between these domains, enabling traffic generated in simulation to interact with emulated devices, and vice-versa, creating a unified testing environment.

AgenticNet employs synchronization mechanisms to maintain temporal alignment between its simulation and emulation domains. This is achieved by periodically exchanging state information – specifically, packet timestamps and network conditions – across the Bridge Link connecting the two domains. The emulation domain, representing real-world hardware, drives the simulation timeline, ensuring simulated traffic reflects observed emulation behavior. Conversely, simulated events influencing network state are fed back to the emulation domain, allowing for realistic propagation delay and loss modeling. This bi-directional synchronization is critical for accurately assessing network performance under complex and dynamic conditions, as it prevents discrepancies between the virtual and physical network environments and enables comprehensive testing of control plane and data plane functionality.

Implementation Details: Python, C++, and the Pursuit of Speed

AgenticNet utilizes a hybrid implementation strategy, employing Python for high-level control functions and C++ for performance-critical components. This approach leverages Python’s rapid prototyping capabilities and extensive libraries for network topology definition, experiment management, and data analysis. Simultaneously, the computational demands of packet processing and forwarding are handled by C++ modules, resulting in substantial performance gains. This division of labor allows for both flexibility in experimentation and the efficient simulation of large-scale network traffic; for instance, a RateLimitForwarder implemented in C++ completed a full day of virtual network traffic in 4.3 seconds, a significant improvement over the 100 seconds required by a Python-only implementation.

The AgenticNet API facilitates the programmatic definition of network configurations and traffic generation, enabling the creation of repeatable experimental setups. This API allows users to specify network topologies – the arrangement of nodes and links – and define traffic patterns, including packet rates, sizes, and destinations, all through code. By defining these parameters programmatically, researchers and developers can reliably reproduce experiments, ensuring consistent results and facilitating thorough testing of network protocols and security mechanisms. The resulting configurations are stored as code, allowing for version control and easy sharing of experimental setups within a team or the wider research community.

AgenticNet allows for the deployment of custom forwarding logic within the simulated network environment, enabling security feature testing such as rate limiting. A specific implementation, the RateLimitForwarder within a SwitchNode, was benchmarked processing a full day of virtual network traffic. The C++ implementation of this forwarder completed the task in 4.3 seconds, while the equivalent Python implementation required 100 seconds. This represents an 8.6 to 9.5 times performance improvement for the C++ version, demonstrating the benefits of utilizing C++ for performance-critical network functions within the simulation.

Automated Experimentation: Unleashing LLMs for Network Resilience

AgenticNet introduces a novel architecture designed to seamlessly integrate Large Language Model (LLM)-based agents, exemplified by systems like Claude, into the traditionally manual process of network experimentation. This integration moves beyond simple scripting by enabling agents to autonomously design, configure, and execute complex network tests. Through the AgenticNet API, these agents can dynamically define network topologies, generate realistic traffic patterns, and rigorously analyze the resulting data – significantly accelerating research and development cycles. The system facilitates a closed-loop process where agents can learn from experimental outcomes, refine their configurations, and iteratively optimize network performance, representing a shift towards self-driving network research.

AgenticNet facilitates a significantly accelerated research and development cycle through the integration of large language model (LLM)-based agents. These agents leverage the AgenticNet API to autonomously manage the entire experimental process, beginning with network topology configuration and progressing to realistic traffic generation. Critically, the agents aren’t limited to simply running experiments; they also independently analyze the resulting data, identifying key performance indicators and potential areas for optimization. This closed-loop automation reduces the need for manual intervention, allowing researchers to explore a wider range of network designs and configurations in a fraction of the time, ultimately fostering innovation and more robust network solutions.

AgenticNet’s automated experimentation capabilities significantly accelerate network protocol development and bolster defenses against malicious attacks. Testing revealed the system effectively blocked over 93% of flood attacks while simultaneously maintaining a 100% pass rate for legitimate traffic – a critical balance often compromised in security implementations. This high level of performance was achieved with minimal impact on network latency, demonstrated by a remarkably low 2”s error in Round-Trip Time (RTT). The ability to rapidly prototype, test, and refine protocols in an automated fashion promises a new paradigm for network resilience and innovation, allowing researchers and developers to proactively address emerging threats and optimize network performance with unprecedented speed.

AgenticNet embodies a distinctly pragmatic approach to network experimentation, mirroring a philosophy of active interrogation. The system doesn’t simply accept the limitations of existing tools; it actively seeks to bypass them through the intelligent application of AI agents. This resonates with John McCarthy’s assertion: “Every worthwhile endeavor has an element of risk.” The tool’s core strength-combining simulation and emulation-inherently involves navigating the complexities and potential instabilities of a hybrid system. Just as McCarthy advocated pushing boundaries, AgenticNet doesn’t shy away from this challenge, instead leveraging AI to manage and even exploit the inherent risks for increased flexibility and performance in network analysis.

Beyond the Scaffold

AgenticNet proposes a compelling, if temporary, truce between the ideal worlds of network simulation and the messy realities of emulation. The system’s value lies not in solving the inherent tension between these approaches – that problem remains stubbornly fundamental – but in shifting the locus of control. It is not enough to merely run experiments; the architecture invites a dismantling of the experimental process itself. Future iterations should not focus on perfecting the agents’ coding abilities, but on their capacity for genuine, unpredictable exploration-allowing them to formulate hypotheses, design tests, and interpret failures with minimal human intervention.

The current emphasis on rate limiting, while practical, hints at a deeper constraint. The system operates, as most do, within predefined boundaries. The true test of AgenticNet, or its successors, will be its ability to break those boundaries-to generate network behaviors that were never anticipated by its creators. Only then will it reveal the hidden architectures governing network dynamics, and perhaps, expose the limits of the models upon which it relies.

One anticipates a natural progression toward agents capable of self-modification, evolving their experimental protocols based on observed results. This raises the specter of runaway experimentation, of course, but chaos, after all, is merely a mirror reflecting unseen connections. The goal is not to control the system, but to understand the rules by which it governs itself – even if that understanding arrives through deliberate, controlled demolition.

Original article: https://arxiv.org/pdf/2603.23763.pdf

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

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2026-03-26 09:22