Building AWS Networks with Generative AI: A New Approach

Revolutionizing AWS Networking with Generative AI

In today’s fast-paced cloud environment, network architects, engineers, and cloud teams face the challenge of designing, deploying, and managing complex Amazon Web Services (AWS) networking infrastructures at scale. As the landscape evolves, the introduction of generative AI technologies, particularly Amazon Bedrock and Amazon Q, presents unparalleled opportunities to tackle these challenges innovatively. These advanced tools enable quicker deployments of new AWS networks and modernization of existing infrastructures, making the lives of network professionals much easier.

Streamlining the Network Lifecycle with AI

This article dives into real-world examples primarily focusing on greenfield networks, showcasing how generative AI tools assist across the entire network lifecycle—from discovery and implementation to testing, rollback, and optimization. Here’s what we’ll cover:

1. Building an AWS Cloud WAN global network using Amazon Q Developer CLI Agentic AI
2. Updating AWS Cloud WAN global network using Amazon Bedrock
3. Modernizing AWS networks with Amazon VPC Lattice and Agentic AI
4. Building AWS networks with Infrastructure as Code (IaC) using Amazon Q
5. Analyzing AWS networks using Amazon Bedrock

Let’s begin with the first use case.

Build AWS Cloud WAN Global Network Using Amazon Q Developer CLI Agentic AI

An Agentic AI workflow allows an AI agent to autonomously or semi-autonomously complete tasks by making decisions and executing actions. We’ll leverage the Amazon Q command line interface (CLI) to create an AWS Cloud WAN global network in just a few minutes.

For this workflow, we’ll focus on setting up a greenfield AWS Cloud WAN network; however, the process can also update existing networks. Familiarity with key AWS Cloud WAN components—global network, core network, core network policy, and network segments—is assumed.

The goal is to build a global network across three AWS Regions, as depicted below.

Creating the Global Network

After installing the Amazon Q CLI, run the command q chat to initiate a conversation with the agent. We’ll instruct the agent to create the global network, as illustrated in the animation below.

In mere seconds, the agent executes necessary API calls to create the global network and supplies a list of the next steps for setup.

Next Steps in Setup

Next, we’ll instruct the agent to establish a core network within this newly created global network, and it automatically discovers the existing configurations.

After confirming the core network’s creation in the AWS Console, we can proceed to establish a new AWS Cloud WAN policy with another prompt:

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Create a new Core Network Policy, use BGP ASN range 64512-64612, Deploy Cloud WAN in 3 Edge locations: us-east-1, ap-northeast-1, eu-central-1. Create default segment with segment name as Production.

The Amazon Q CLI Agent executes multiple API commands, ensuring the policy’s creation aligns with our original intent.

With a few natural language prompts, we established the AWS Cloud WAN global network within minutes. The tool’s ability to process multi-step requirements while being context-aware showcases the immense power of generative AI in network setup.

Updating AWS Cloud WAN Global Network Using Amazon Bedrock

Now, let’s explore how we can use Amazon Bedrock to update an existing AWS Cloud WAN network. First, we’ll import the existing AWS Cloud WAN JSON policy into the Amazon Bedrock LLM chat/playground section.

With Amazon Bedrock, you can choose from multiple foundation models to suit your needs. For this example, we’ll utilize Anthropic’s Claude 3.7 Sonnet model.

Imagine you want to add a segment named SDWAN to the existing policy. The prompt provided could look like this:

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Attached is an AWS Cloud WAN JSON policy, create a new policy which should add a new segment, name it SDWAN, and implement it in all 3 edge locations us-east-1, eu-central-1, and ap-northeast-1.

Amazon Bedrock swiftly generates the new AWS Cloud WAN JSON policy based on the input prompt. The configuration difference between the old and new policies can also be observed, facilitating smooth rollbacks if necessary.

Modernizing AWS Networks Using Amazon VPC Lattice and Agentic AI

As companies strive to modernize their networks, many face hurdles such as resource constraints and learning curves. Using the Amazon Q Developer CLI agent, however, users can access expert knowledge tailored to their specific AWS accounts.

Picture this: a system of VPCs linked by a Transit Gateway. You want to transition to Amazon VPC Lattice for enhanced capabilities. By querying the Amazon Q Developer CLI agent, it provides an overview of your existing network, shedding light on potential migration steps.

The agent develops a phased migration plan to minimize downtime based on your existing configuration, streamlining both the planning and execution of the migration process.

Building AWS Networks with IaC Using Amazon Q

If you already have an Infrastructure as Code (IaC) or DevOps practice in place, utilizing Amazon Q in integrated development environments (IDEs) like Visual Studio Code can be a natural next step.

For instance, you might prompt Amazon Q Developer CLI to generate a CloudFormation template for a well-architected Amazon VPC. Not only does it create a comprehensive template, but it also summarizes all relevant resources, facilitating a quicker setup.

This approach allows you to iteratively build your infrastructure while maintaining clarity and control over configurations.

Analyzing AWS Networks Using Amazon Bedrock

Lastly, Amazon Bedrock enhances network diagram analysis drastically. By using flexible models from various AI providers, it automates the interpretation of complex network designs, identifies vulnerabilities, and suggests optimizations.

You can input your AWS network diagrams into tools powered by Amazon Bedrock that analyze the topology, pinpoint misconfigurations, and present improvements.

After uploading your architecture diagram, you can prompt the AI for explanations and recommendations, making the analysis process more efficient.

By harnessing the capabilities of generative AI tools like Amazon Bedrock and Amazon Q, network professionals can truly revolutionize their approach to AWS networking. The ability to rapidly design, implement, and optimize infrastructures will not only accelerate deployment cycles but also enhance overall network performance and security.

You’re now ready to explore these solutions within your AWS environment, paving the way for a more innovative network architecture. Don’t hesitate to take the leap into generative AI—it’s a game-changer in the quest for optimal network management.