AI AGENTS & NETWORK TROUBLESHOOTING
Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting
Artificial Intelligence (AI) and Large Language Models (LLMs), are increasingly finding application in network-related tasks, such as network configuration synthesis [22] and dialogue-based interfaces to network measurements [23], among others. In this preliminary work, we restrict our focus to the application of AI agents to network troubleshooting and elaborate on the need for a standardized, reproducible, and open benchmarking platform, where to build and evaluate AI agents with low operational effort. This platform primarily aims at standardize and democratize the experimentation with AI agents, by enabling researchers and practitioners - including non-domain experts such as ML/AI engineers- to evaluate AI agents on curated problem sets, without concerns for underlying operational complexities. We present a modular and extensible benchmarking framework that supports widely adopted network emulators [3, 18, 20, 21]. It targets an extensible set of network issues in diverse real-world scenarios - e.g., data centers, access, WAN, etc. - and orchestrates the end-to-end evaluation workflows, including failure injection, telemetry instrumentation and collection, and agent performance evaluation. Agents can be easily connected through a single Application Programming Interface (API) to an emulation platform and rapidly evaluated. The code is publicly available at https://github.com/zhihao1998/LLM4NetLab.
Executive Impact: Democratizing AI for Network Operations
AI agents and LLMs are revolutionizing network operations, particularly in troubleshooting. However, current evaluation methods lack standardization, reproducibility, and a common platform. This paper introduces a novel benchmarking framework that addresses these gaps. It provides a modular, extensible platform supporting major network emulators and diverse real-world scenarios. By simplifying agent integration via a single API and orchestrating end-to-end evaluation workflows, the framework democratizes AI agent experimentation, allowing even non-domain experts to develop and assess advanced troubleshooting solutions.
0%
Operational Effort Reduction
0
Emulators Supported
0
Workflow Steps Covered
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Complexity of Network Troubleshooting
Network engineers face cumbersome and mechanical steps to diagnose and mitigate issues, from identifying telemetry signals to iterating on root-cause hypotheses. This manual process is complex, slow, and error-prone, requiring expert operators to reason across multiple dimensions.
Modern telemetry from programmable data planes, such as sketches [10, 16] and in-band network telemetry (INT) [17], introduces new degrees of freedom but at the cost of greater operational complexity. Human intervention remains a primary bottleneck, hindering "just-in-time" orchestration of measurements.
Key Insight: "This manual process is still complex, slow and error-prone, as it requires expert operators to reason across multiple dimensions."
Our Benchmarking Framework in Action
We introduce a modular and extensible benchmarking framework designed for AI agents in network troubleshooting. It aims to standardize and democratize experimentation by abstracting operational complexities.
The framework supports widely adopted network emulators [3, 18, 20, 21] and targets diverse real-world scenarios. It orchestrates end-to-end evaluation workflows, including failure injection, telemetry collection, and agent performance evaluation. Agents can connect easily via a single API for rapid evaluation.
Key Insight: "We present a modular and extensible benchmarking framework that supports widely adopted network emulators [3, 18, 20, 21]."
Future Directions: Evolving AI Agent Evaluation
Our future work focuses on three key areas: benchmark curation, agent-environment interfaces, and automated assessment of agent behavior.
Benchmark curation involves generating diverse failure scenarios across heterogeneous networks and automating complexity tuning. Unified agent-environment interfaces will abstract low-level complexities and expose structured access to telemetry and control, leveraging MCP-based tools. Finally, we plan to extend the framework with automated behavioral checkups, potentially using LLMs-as-a-judge [12], to evaluate agent reasoning trajectories holistically.
Key Insight: "We aim to curate a diverse benchmark of failure scenarios, spanning heterogeneous networks... We plan to study how to automate the generation of these variations."
0.0
Existing Holistic Platforms
Existing experimentation environments are often limited in scope, lacking standardized and reproducible benchmarks, hindering progress in AI agent development for network troubleshooting.
Enterprise Process Flow
AI Agent Logic
→
Evaluator Metrics & Telemetry
→
Orchestration & Failure Injection
→
Network Environment (Emulator)
Our platform streamlines the AI agent development and evaluation process, enabling a clear, iterative workflow from agent logic to real-time network interaction.
| Feature | Legacy Systems | Our Platform |
|---|---|---|
| Standardization | Limited |
|
| Reproducibility | Challenging |
|
| Operational Effort | High (Custom Code) |
|
| Emulator Support | Fragmented (Specific) |
|
| Real-time Interaction | Static/Offline |
|
| Problem Diversity | Narrow |
|
Unlike legacy systems, our platform provides a unified, interactive environment crucial for evaluating dynamic AI agents in network troubleshooting.
Case Study: AI Agent Localizes Lossy Link
Our Proof-of-Concept demonstrates an AI agent successfully triaging a network issue within our framework. Using a DeepSeek-R1-0528 agent, the platform simulated a lossy link scenario across four BMv2 switches.
The agent, through active probing and telemetry analysis, successfully localized the fault to a specific switch (s3), showcasing the framework's capability for dynamic, interactive troubleshooting.
This highlights the platform's potential to accelerate AI agent development by providing realistic, interactive testing environments and objective evaluation metrics.
Calculate Your Potential AI Impact
Estimate the transformative power of a democratized AI experimentation platform on your organization's network operations and troubleshooting efficiency.
Estimated Annual Savings
$0
Estimated Annual Hours Reclaimed
0
Your Journey to Advanced AI Network Troubleshooting
Our structured approach ensures a seamless transition to leveraging AI agents for robust network observability and troubleshooting.
Phase 1: Discovery & Assessment
We begin by understanding your current network infrastructure, existing troubleshooting workflows, and identifying key pain points where AI can provide the most impact. This involves detailed consultations and data analysis.
Phase 2: Platform Integration & Customization
Our benchmarking framework is integrated with your emulation environments. We customize problem sets and telemetry configurations to mirror your real-world scenarios, ensuring relevant and robust AI agent training.
Phase 3: AI Agent Development & Iteration
Leveraging the democratized platform, your teams (or ours) develop and rapidly iterate on AI agents. The framework provides real-time feedback and evaluation metrics to accelerate the development cycle and optimize agent performance.
Phase 4: Validation & Deployment Strategy
Thorough validation of AI agent performance against curated benchmarks. We work with you to define a clear strategy for phased deployment, continuous monitoring, and ongoing optimization of AI-driven troubleshooting in your live network.
Ready to Transform Your Network Operations?
Don't let complex network issues slow you down. Discover how our platform can empower your team with intelligent, automated troubleshooting.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat