AI Assisted Networking Lab: Embracing AI for Next-Generation Configuration

In this project, I take a fresh approach to building my network lab by harnessing the power of AI. My goal is to refresh and expand my network knowledge while exploring how modern tools like Aider can streamline and revolutionize the way I develop and maintain network configurations in a Git repository. Aider assists with tasks like generating configuration templates, validating network designs, and automating routine configuration changes.

By integrating AI into traditional CCIE lab exercises, I not only dynamically edit configurations but also investigate if the breakthroughs seen in Python code editing with models like Sonnet 3.5 and 3.7 (advanced language models optimized for code generation) are replicable in networking. With this post, I begin my journey toward harnessing current, platform-specific insights that transform my approach to network engineering.

This post begins a series where I will take you through every step of bringing my lab to life—from setting up the physical infrastructure and configuring the repository to leveraging a Git-based workflow for managing and deploying configurations. I plan to integrate AI at every stage of this journey, not only to optimize configuration edits but also to enhance the documentation process, ensuring that every step is clear, reproducible, and future-proof.

Selecting the Simulation Platform and Images

I want to keep my lab environment as close to Git as possible, making containerlab the ideal choice. Containerlab's "docker-compose"–like workflow aligns perfectly with a Git-centric approach while providing a robust foundation for network simulation.

Beyond just simulating networks, containerlab offers the flexibility to integrate AI at every stage. With Aider, I can not only enhance the design, configuration, and automation of the lab but also streamline the generation of templated files—such as the topology definition. Having previously worked with containerlab, I appreciate its simplicity and how well it fits into my workflow, making it a natural fit for this AI-assisted experiment.

• Lab as Code (IaC) Approach: Define your lab topology using concise, declarative files that integrate seamlessly into a Git workflow.
• Lab Orchestration: Efficiently deploy, manage, and tear down your network environments with simple CLI commands.
• Simplicity and Convenience: Enjoy a Docker Compose–like workflow that makes setup fast and user-friendly.
• Scalable Topology Generator: Easily design and expand complex network topologies to suit varied lab scenarios.

Image Selection

For this lab, I'm evaluating network operating system images that balance feature richness with practical deployment considerations. My background in CCIE routing and switching (circa 2014) provides me with a wealth of Cisco-oriented materials, but I'm also exploring newer options to expand my skillset.

Cisco IOS on Linux (IOL)

Cisco IOS on Linux offers several compelling advantages for my lab environment:

• Resource Efficiency: IOL runs as a lightweight process instead of a full VM, making it ideal for resource-constrained environments
• Configuration Compatibility: Maintains high compatibility with physical Cisco devices, allowing me to reuse my existing CCIE materials and config templates
• Feature Set: Supports core L2/L3 features including OSPF, BGP, MPLS, and most switching protocols I need for my lab exercises
• Integration Potential: Works well with containerlab's automation capabilities while preserving the familiar Cisco CLI experience

Arista cEOS

As an alternative, Arista's containerized EOS (cEOS) presents some interesting benefits:

• Modern Architecture: Built specifically for containerized environments with excellent performance characteristics
• Industry Relevance: Provides exposure to an increasingly popular platform in modern data centers
• EOS Advantages: Offers a Linux-based network OS with powerful automation capabilities via eAPI and Python libraries
• Knowledge Transfer: Allows me to translate my Cisco knowledge to a new platform while maintaining many familiar concepts
• GitOps Compatibility: Excellent support for configuration as code paradigms that align with my repository-based approach

Other Considerations

I'm also keeping an eye on Nokia SR Linux and Juniper cRPD as potential additions to create a multi-vendor environment. This would provide a more realistic enterprise scenario where I could practice interoperability configurations.

Looking Ahead

In my next post, I'll share the details about my final image selection and how it performs in real-world lab scenarios. I'll also walk through my complete lab provisioning setup, including the infrastructure components, connectivity requirements, and the automation frameworks I'm implementing to manage the environment. This will provide a practical foundation for anyone looking to replicate this AI-assisted approach to network lab configuration and management.

Through this series, we'll explore how AI can transform traditional networking practices into modern, efficient workflows that leverage the best of both automation and human expertise. Stay tuned as we build a next-generation networking lab environment together.