Accelerate IaC Troubleshooting with Amazon Bedrock Agents

Troubleshooting Infrastructure as Code (IaC) Errors with Amazon Bedrock Agents

Troubleshooting infrastructure as code (IaC) errors often consumes valuable time and resources. Developers can spend multiple cycles searching for solutions across forums, troubleshooting repetitive issues, or trying to identify the root cause. These delays can lead to missed security errors or compliance violations, especially in complex, multi-account environments.

This post demonstrates how you can use Amazon Bedrock Agents to create an intelligent solution to streamline the resolution of Terraform and AWS CloudFormation code issues through context-aware troubleshooting. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Our solution uses Amazon Bedrock Agents to analyze error messages and code context, generating detailed troubleshooting steps for IaC errors. In organizations with multi-account AWS environments, teams often maintain a centralized AWS environment for developers to deploy applications. This setup ensures that AWS infrastructure deployments using IaC align with organizational security and compliance measures. For specific IaC errors related to these compliance measures, such as those involving service control policies (SCPs) or resource-based policies, our solution intelligently directs developers to contact appropriate teams like Security or Enablement. This targeted guidance maintains security protocols and ensures that sensitive issues are handled by the right experts.

The solution is flexible and can be adapted for similar use cases beyond these examples.

Solution Overview

Before we dive into the deployment process, let’s walk through the key steps of the architecture as illustrated in the following figure.

The workflow for the Terraform solution is as follows:

1. Initial input through the Amazon Bedrock Agents chat console – The user begins by entering details about their Terraform error into the chat console for Amazon Bedrock Agents. This typically includes the Terraform Cloud workspace URL where the error occurred, and optionally, a Git repository URL and branch name if additional context is needed.
2. Error retrieval and context gathering – The Amazon Bedrock agent forwards these details to an action group that invokes the first AWS Lambda function (see the following Lambda function code). This function invokes another Lambda function (see the following Lambda function code) which retrieves the latest error message from the specified Terraform Cloud workspace. If a Git repository URL is provided, it also retrieves relevant Terraform files from the repository. This contextual information is then sent back to the first Lambda function.
3. Error analysis and response generation – Lambda function would then construct a detailed prompt that includes the error message, repository files (if available), and specific use case instructions. It then uses the Amazon Bedrock model to analyze the error and generate either troubleshooting steps or guidance to contact specific teams.
4. Interaction and user guidance – The agent displays the generated response to the user. For most Terraform errors, this includes detailed troubleshooting steps. For specific cases related to organizational policies (for example, service control policies or resource-based policies), the response directs the user to contact the appropriate team, such as Security or Enablement.
5. Continuous improvement – The solution can be continually updated with new specific use cases and organizational guidelines, making sure that the troubleshooting advice stays current with the organization’s evolving infrastructure and compliance requirements. For example:
   1. SCP or IAM policy violations – Guides developers when they encounter permission issues due to SCPs or strict AWS Identity and Access Management (IAM) boundaries, offering alternatives or escalation paths.
   2. VPC and networking restrictions – Flags non-compliant virtual private cloud (VPC) or subnet configurations (such as public subnets) and suggests security-compliant adjustments.
   3. Encryption requirements – Detects missing or incorrect encryption for Amazon Simple Storage Service (Amazon S3) or Amazon Elastic Block Store (Amazon EBS) resources and recommends the appropriate configurations to align with compliance standards.

Test the Solution

The following example is of a Terraform error due to a service control policy. The troubleshooting steps provided would be aligned to address those specific constraints. The action group triggers the Lambda function, which follows structured single-shot prompting by passing the complete context—such as the error message and repository contents—in a single input to the Amazon Bedrock model to generate precise troubleshooting steps.

Clean Up

The services used in this demo can incur costs. Complete the following steps to clean up your resources:

1. Delete the Lambda functions if they are no longer required.
2. Delete the action group and Amazon Bedrock agent you created.

Conclusion

IaC offers flexibility for managing cloud environments, but troubleshooting code errors can be time-consuming, especially in environments with strict organizational guardrails. This post demonstrated how Amazon Bedrock Agents, combined with action groups and generative AI models, streamlines and accelerates the resolution of Terraform errors while maintaining compliance with environment security and operational guidelines.

Using the capabilities of Amazon Bedrock Agents, developers can receive context-aware troubleshooting steps tailored to environment-related issues such as SCP or IAM violations, VPC restrictions, and encryption policies. The solution provides specific guidance based on the error’s context and directs users to the appropriate teams for issues that require further escalation. This reduces the time spent on IaC errors, improves developer productivity, and maintains organizational compliance.

About the Authors

Akhil Raj Yallamelli is a Cloud Infrastructure Architect at AWS, specializing in architecting cloud infrastructure solutions for enhanced data security and cost efficiency. He is experienced in integrating technical solutions with business strategies to create scalable, reliable, and secure cloud environments. Akhil enjoys developing solutions focusing on customer business outcomes, incorporating generative AI (Gen AI) technologies to drive innovation and cloud enablement. He holds an MS degree in Computer Science. Outside of his professional work, Akhil enjoys watching and playing sports.

Ebbey Thomas is a Senior Generative AI Specialist Solutions Architect at AWS. He designs and implements generative AI solutions that address specific customer business problems. He is recognized for simplifying complexity and delivering measurable business outcomes for clients. Ebbey holds a BS in Computer Engineering and an MS in Information Systems from Syracuse University.