Use of agents for Amazon Bedrock in the interactive generation of infrastructure as code.

In the dynamic world of cloud infrastructure, Agents for Amazon Bedrock emerge as an innovative proposal for teams looking to optimize their infrastructure as code (IaC) processes. This Amazon tool is designed to automate prompt engineering and task organization requested by users. Once configured, the agent builds the prompt and supplements it with specific company information, providing responses in natural language.

This advancement allows Amazon Bedrock agents to accept cloud architecture diagrams, analyze them automatically, and generate Terraform or AWS CloudFormation templates. The solution employs generative adversarial network (GAN) to ensure that the generated scripts meet organizational needs and industry standards. A key aspect of these agents is their ability to dynamically interact with users, completing IaC configuration accurately by requesting necessary additional information.

Amazon Bedrock is a fully managed service offering a variety of high-performance base models from leading AI companies through a single API, along with the capabilities needed to build generative AI applications with security and privacy.

This innovative solution focuses on generating customized IaC scripts that are compatible with each organization’s specific standards from loaded architecture diagrams. This not only accelerates deployments and reduces errors but also ensures compliance with security guidelines.

To better understand the deployment process, it is helpful to know the key steps of the architecture illustrated in an attached diagram.

1. Initial entry through Amazon Bedrock chat console: The user enters the name of the Amazon S3 bucket and the object where the architecture diagram is stored.
2. Diagram analysis and query generation: The Amazon Bedrock agent sends the diagram location to an action group that invokes an AWS Lambda function, which analyzes the diagram and generates a summary along with questions about missing components or necessary parameter values.
3. User interaction and confirmation: The agent displays the generated questions to the user and records their responses. It then provides a summary of the diagram and asks the user for their approval or necessary adjustments.
4. IaC generation and deployment: A second action group invokes a Lambda function that processes the entered data and the organization’s coding guidelines to create the IaC, which is automatically sent to a GitHub repository.

To deploy the solution, it is recommended to follow these four steps:

1. Set up a Knowledge Base (KB) in Amazon Bedrock: Create a KB that offers standardized Terraform modules from the organization.
2. Configure the Bedrock agent: Create an agent with appropriate permissions and adjust the instructions for IaC creation.
3. Set up action groups in the agent: Establish Lambda functions to analyze diagrams and generate queries, as well as to create and deploy Terraform code.
4. Assign action groups to the agent: Link the Lambda functions correctly and provide the necessary S3 URI with the API schema.

Amazon Bedrock agents use generative AI to transform architecture diagrams into IaC scripts, facilitating deployments on AWS and ensuring they comply with best practices from the start. This tool not only streamlines the cloud adoption process but also significantly improves ongoing infrastructure management. Additionally, its interactive capability can be applied to various AWS services, offering a comprehensive solution to optimize cloud infrastructure.

Akhil Raj Yallamelli, cloud infrastructure architect at AWS, and Ebbey Thomas, generative AI solution specialist, have significantly contributed to the development of these technologies. Their expertise and innovative approach ensure that AWS users can benefit from advances in cloud infrastructure automation and optimization.