Author: Denis Avetisyan
Researchers have created a comprehensive platform to rigorously evaluate the capabilities of artificial intelligence agents in diagnosing and resolving complex network issues.
This paper introduces NIKA, a framework for benchmarking AI agents on network troubleshooting using curated incidents, orchestration, and realistic network emulation.
Despite advances in agentic systems for network management, evaluating the performance of large language model (LLM) agents in dynamic network troubleshooting remains challenging due to a lack of standardized benchmarks. This paper introduces NIKA, ‘A Network Arena for Benchmarking AI Agents on Network Troubleshooting’, a comprehensive framework designed to address this gap by providing a curated suite of network incidents and an orchestration platform for rapid agent prototyping. Our evaluation reveals that while larger models demonstrate improved issue detection, pinpointing fault localization and root cause analysis continues to pose a significant hurdle. Will NIKA facilitate the development of more robust and reliable AI-driven solutions for proactive network management and automated incident resolution?
The Inherent Flaws of Manual Network Incident Response
Historically, resolving network incidents has been a painstakingly manual process, demanding seasoned engineers to sift through logs, analyze traffic, and correlate events – a workflow susceptible to human error and significant delays. This reliance on individual expertise creates bottlenecks, particularly as network architectures grow increasingly complex and the volume of data generated by those networks explodes. Consequently, even seemingly minor disruptions can escalate into prolonged outages, impacting productivity and potentially causing substantial financial losses. The inherent limitations of manual troubleshooting struggle to keep pace with the speed at which modern networks operate and the ever-present threat of sophisticated cyberattacks, necessitating a shift towards more automated and efficient response mechanisms.
The escalating intricacy of contemporary networks – driven by virtualization, cloud computing, and the proliferation of interconnected devices – has rendered traditional, manual approaches to incident response increasingly unsustainable. Networks are no longer static entities; they are dynamic, distributed systems where failures can manifest rapidly and propagate quickly. Consequently, a shift towards automated solutions is paramount. These systems leverage technologies like machine learning and network analytics to detect anomalies, diagnose root causes, and initiate remediation steps with minimal human intervention. Automation not only accelerates response times, crucial for mitigating damage from security breaches or service disruptions, but also reduces the potential for human error in high-pressure situations, enabling organizations to maintain operational resilience in the face of growing cyber threats and increasingly complex infrastructure.
Effective network incident response hinges on rigorous testing and development of mitigation strategies, yet faithfully recreating real-world network conditions proves remarkably difficult. Simply simulating traffic volume isn’t enough; a truly representative environment must account for the unpredictable nature of user behavior, the diversity of network devices, and the subtle interactions between countless protocols. Building such a testbed requires substantial investment in both hardware and specialized software, alongside the ongoing effort to maintain its accuracy as live networks evolve. Furthermore, concerns regarding data privacy and security often restrict the use of production traffic for testing purposes, forcing developers to rely on synthetic data which may not fully capture the nuances of genuine network events. Consequently, many organizations struggle to validate their incident response plans against realistic scenarios, increasing the risk of prolonged outages and significant financial losses when real-world attacks occur.
Formalizing the Problem: The Incident Specification
An Incident Specification serves as a formalized description of a network problem, comprising three core elements. The first is a defined Network Scenario, establishing the context in which the issue occurs. Second, the specification details the specific Network Issue – the observed malfunction or deviation from expected behavior. Finally, a realistic Traffic Workload is included, defining the volume and characteristics of network traffic present during the incident. This holistic encapsulation ensures that incidents are defined not only by what went wrong, but also by how and under what conditions, providing a comprehensive basis for automated troubleshooting and validation.
The Network Scenario relies on a defined Network Topology which details the arrangement of network devices – including routers, switches, firewalls, and servers – and the links connecting them. This topology isn’t merely a visual diagram; it’s a precise specification of device types, quantities, and interconnections, often represented as a graph data structure. Accurate representation includes specifying link capacities, propagation delays, and any configured redundancy. The topology serves as the foundational model for simulating network behavior and is critical for ensuring that incidents are reproduced in a controlled and repeatable manner, mirroring the production environment’s structure and connectivity.
Consistent incident reproduction is achieved by defining incidents through a standardized specification encompassing network topology, the specific network issue, and a representative traffic workload. This allows for the creation of repeatable test cases, critical for verifying the functionality and reliability of automated remediation workflows. Rigorous testing, facilitated by reproducible incidents, ensures that automated solutions perform as expected under defined conditions, identifies potential failure points, and validates the effectiveness of the automation before deployment in a production environment. The ability to consistently recreate incidents also supports performance benchmarking and optimization of automated responses.
The Emergence of AI-Driven Network Troubleshooting
The AI Agent utilizes telemetry data – encompassing network performance metrics, device status, and configuration information – to establish a baseline of normal network behavior. Deviations from this baseline are flagged as potential issues, enabling proactive incident response before user impact. This data-driven approach moves beyond reactive troubleshooting, allowing the agent to predict and prevent failures by identifying anomalous patterns and correlating events across the network infrastructure. The agent analyzes data streams from various network elements, including routers, switches, and servers, to build a comprehensive understanding of network health and performance, facilitating faster diagnosis and resolution of issues.
The AI Agent utilizes Management and Control Plane (MCP) tools to autonomously collect network data and execute diagnostic procedures. This includes employing tools for packet capture, traceroute analysis, and device configuration retrieval. Automation of these tasks reduces manual intervention required for troubleshooting, allowing the agent to identify and isolate issues more rapidly. Specifically, the agent leverages MCP tools to verify device reachability, assess link status, and gather performance metrics such as latency and throughput, forming the basis for its diagnostic reasoning.
The AI Agent’s capabilities are significantly enhanced through the integration of Large Language Model (LLM)-based agents, which provide advanced reasoning and decision-making functionalities beyond traditional telemetry analysis. Internal testing, conducted using the NIKA network simulation platform, demonstrates an overall detection accuracy of approximately 90-95% across a range of network sizes and simulated failure scenarios. This detection rate represents the agent’s ability to correctly identify the presence of a network issue, while further analysis is required to pinpoint the specific root cause and location of the problem. The LLM component enables the agent to correlate disparate data points and infer potential issues that might be missed by simpler rule-based systems.
Detection of network anomalies by the AI Agent demonstrates high overall accuracy; however, the precision with which these anomalies are localized varies considerably depending on the nature of the issue. Performance metrics indicate a localization accuracy of up to 97% for physical link failures, allowing for rapid identification of the affected segment. Conversely, localization accuracy drops to approximately 58% for issues related to resource contention, such as CPU or memory bottlenecks. This discrepancy suggests the AI Agent’s current algorithms are more effective at identifying discrete physical layer problems than diagnosing complex, dynamic issues involving shared resources.
NIKA: A Standardized Framework for Network Diagnostic Evaluation
NIKA establishes a crucial, standardized framework for objectively assessing the capabilities of AI Agents when confronted with a spectrum of network incidents. This platform moves beyond anecdotal evidence by providing a consistent environment for evaluating agent performance across meticulously defined ‘Incident Specifications’ – ranging from simple connectivity failures to complex security breaches. By utilizing these standardized scenarios, NIKA allows for direct comparison of different AI models, facilitating quantifiable progress in automated network troubleshooting and resilience. The ability to test against a diverse set of incidents ensures that AI Agents are not merely optimized for specific situations, but demonstrate genuine adaptability and robustness in real-world network environments, ultimately paving the way for more reliable and proactive network management.
NIKA leverages network emulation to construct meticulously crafted, yet entirely virtualized, testing grounds for evaluating AI agent performance. This approach allows researchers to simulate complex network incidents and observe agent responses without the risks associated with live network experimentation – preventing potential disruptions or security breaches. By replicating real-world network conditions, including traffic patterns and device behaviors, NIKA ensures the validity and reproducibility of test results. The emulated environments are fully isolated, enabling comprehensive testing of agent capabilities in a controlled and safe manner, and facilitating the consistent benchmarking of different AI models against identical incident specifications. This capability is crucial for reliable performance assessment and the development of robust AI-driven network solutions.
The architecture of NIKA incorporates a dedicated Agent Access Layer, a critical component designed to safeguard network infrastructure during automated testing and analysis. This layer functions as a secure intermediary, strictly controlling the interactions between the AI agent and the emulated network environment; it prevents unintended or malicious actions that could compromise system stability or data integrity. By encapsulating network resources and enforcing granular permission controls, the Agent Access Layer ensures that all agent activities remain within defined boundaries, effectively mitigating risks associated with autonomous operation. This controlled access not only enhances the overall reliability of the testing process but also establishes a foundational element for deploying AI-driven network management solutions in live production environments, fostering trust in their ability to operate safely and predictably.
The NIKA framework significantly advances the field of network diagnostics through automated root cause analysis, delivering a 2.5-fold improvement in accuracy when utilizing GPT-5 over smaller language models. By systematically emulating network incidents and providing a controlled environment for AI agents, NIKA moves beyond traditional, manual troubleshooting methods. This automation isn’t merely about speed; it’s about precision, generating quantifiable metrics that objectively assess diagnostic performance and pinpoint the source of network issues with greater reliability. Testing within NIKA demonstrates that GPT-5’s enhanced reasoning capabilities translate directly into more accurate RCA, offering a powerful tool for network engineers seeking to minimize downtime and optimize network health.
Analysis within the NIKA framework reveals a substantial increase in reasoning complexity for GPT-5, evidenced by its consumption of 105,000 input tokens and generation of 14,600 output tokens during incident analysis-a significant leap compared to smaller models. This heightened cognitive load is coupled with remarkably low error rates in tool invocation, registering at just 0.7% for GPT-5; in contrast, the GPT-5-mini model exhibited a 1.6% error rate. These metrics suggest that GPT-5 not only processes significantly more information when diagnosing network incidents but also demonstrates a considerably more reliable capacity to utilize external tools effectively, ultimately contributing to its improved accuracy in root cause analysis as observed within the NIKA benchmarking environment.
The pursuit of robust AI agents for network troubleshooting, as detailed in this work regarding the NIKA framework, demands a fundamentally correct approach to problem-solving. Tim Berners-Lee aptly stated, “The Web is more a social creation than a technical one.” This resonates with the necessity for AI agents to not merely function within a network-demonstrating an ability to resolve incidents-but to operate with predictable consistency, mirroring the reliable interconnectedness of the Web itself. NIKA’s curated incident suite and orchestration platform serve to rigorously test these boundaries, ensuring the ‘correctness’ of an agent’s algorithmic response-a provable solution, not simply one that appears to work on a limited dataset.
What’s Next?
The introduction of NIKA represents a necessary, though hardly sufficient, step towards rigorous evaluation of AI agents in the domain of network troubleshooting. The current landscape is awash in demonstrations-agents that ‘work’ on contrived examples. True progress demands a shift in focus: not whether an agent can solve a problem, but how it scales with network complexity. The curated incident suite is a commendable start, but the ultimate test lies in generating incidents with provable characteristics – those exhibiting specific topological vulnerabilities or quantifiable performance bottlenecks.
A critical limitation remains the emulation environment itself. While realistic, emulation is, by definition, an approximation. The inherent complexities of real-world network dynamics – packet loss correlated with time-of-day, subtle queuing effects, the sheer noise of a production network – are exceedingly difficult to replicate faithfully. Future work must address this gap, perhaps through hybrid approaches that combine emulation with limited real-world data injection.
Ultimately, the value of a benchmarking framework isn’t measured in the number of agents it can test, but in its ability to expose fundamental limitations. The pursuit of elegant algorithms – those exhibiting optimal asymptotic behavior – remains paramount. The goal is not simply to create agents that ‘work’, but to understand why they work, and, more importantly, when they will inevitably fail.
Original article: https://arxiv.org/pdf/2512.16381.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-20 13:57