Custom

What is AWS Transform custom?

AWS Transform custom uses agentic AI to perform large-scale modernization of software, code, libraries, and frameworks to reduce technical debt. It handles diverse scenarios including language version upgrades, API and service migrations, framework upgrades and migrations, code refactoring, and organization-specific transformations.

Through continual learning, the agent improves from every execution and developer feedback, delivering high-quality, repeatable transformations without requiring specialized automation expertise.

Key Capabilities

AWS Transform custom provides the following capabilities:

Transformation Patterns

AWS Transform custom supports diverse transformation patterns to address your modernization needs. Each pattern has different complexity characteristics based on the scope and nature of the changes required.

How AWS Transform custom Works

AWS Transform custom is usually used in large-scale projects where multiple codebases or modules are transformed. Teams typically follow a four-phase workflow:

Define Transformation - Provide natural language prompts, documentation, and code samples to the agent, which generates an initial transformation definition. This definition can be iteratively refined through chat or direct edits. This phase can be skipped when using AWS-managed transformations.

Pilot or Proof-of-Concept - Test the transformation on sample codebases and refine based on results. This validation phase helps estimate the cost and effort of the full transformation. Continual learning improves quality during this phase.

Scaled Execution - Set up automated bulk execution using the CLI, with developers reviewing and validating results. Monitor progress using the web application and track transformations across multiple repositories.

Monitor and Review - Continual learning automatically improves the transformation quality. Review and approve knowledge items that have been extracted from executions to ensure they meet quality standards.

Understanding Key Concepts

This section explains key concepts for working with AWS Transform custom.

Transformation Definitions

A transformation definition is a package that contains the instructions and knowledge needed to perform a specific code transformation. It consists of:

AWS Transform CLI automatically downloads transformation definitions to the current directory by when needed for execution, inspection, or modification.

Transformation Registry

The transformation registry is your AWS account's centralized repository for storing and managing transformation definitions. Transformations in the registry can be:

Draft vs Published Transformations

AWS Transform custom supports two states for transformations in the registry:

Draft transformations are in-progress or untested transformation definitions. They are saved as specific versions and can be retrieved, updated, and executed by users referring to that specific version. Drafts are useful for iterative development, testing, and refinement before the transformation is ready to be shared with your team. Drafts are also associated to specific conversation. If you restart the CLI, you can use atx --conversation-id {id} to restore a previous one.

Published transformations are available in your account's transformation registry for execution by other users with the required IAM permissions. Published transformations can be discovered using atx custom def list.

The typical workflow is:

You can also publish a transformation directly without saving it as a draft.

References vs Knowledge Items

AWS Transform custom uses two types of knowledge to improve transformation quality:

References are user-provided documentation that you explicitly add to a transformation definition. References support text files only (maximum 10MB per file) and usually contain documentation, API specifications, migration guides, and code samples. You add references when creating or updating a transformation definition in interactive mode.

Knowledge items are automatically extracted learnings from transformation executions. These are created asynchronously by the continual learning system based on execution trajectories, developer feedback, and code fixes encountered during transformations. Knowledge items start in a "not approved" state and must be explicitly approved by transformation owners before they can be used in future executions. Unlike references which you provide upfront, knowledge items accumulate over time as the transformation is executed across different codebases.

Build and Validation Commands

The build or validation command is an optional parameter that specifies how to validate your code during the transformation process. This command is executed at various points during the transformation to ensure code integrity.

Providing a command that validates the results and returns issues if validation fails is very important to improve transformation quality through continual learning. If no build or validation is needed, use "noop" as the value.

For examples and detailed guidance, see Build and Validation Commands in the Workflows section.

Continual Learning

Continual learning is the system that automatically captures feedback from every transformation execution and improves transformation quality over time. The system gathers information through:

This information is processed to create knowledge items that are added to the transformation definition to improve future transformations. The learning process occurs automatically after transformations are completed, requiring no additional user input.

Introduction to custom transformation commands

Here are a few of the commands you can use with custom transformations. The complete list of commands is in the AWS Transform custom transformations command reference.