Amazon Transform Custom: The Agentic AI Revolution for Code Modernization

Estimated reading time: 12 minutes

Key Takeaways

• • Amazon Transform Custom uses advanced agentic AI to automate large-scale, repeatable code modernization.

• • It accelerates modernization efforts by up to 5× faster while reducing execution time by as much as 80%.

• • The service handles diverse modernization patterns from language upgrades to complex framework migrations and legacy platform shifts.

• • Transform Custom enables organizations to reduce technical debt systematically, freeing developers for more innovative work.
  • Its natural language-driven workflow allows defining, testing, publishing, and executing transformations at scale, supported by continuous learning.

What is Amazon Transform Custom?

Amazon Transform Custom is a capability within the broader AWS Transform service ecosystem designed explicitly for customized and organization-specific code modernizations. Unlike generic AI tools, Transform Custom leverages agentic AI — autonomous, tool-using AI agents — to manage large-scale application and code transformations automatically and intelligently across virtually any programming language, framework, runtime, or architecture.

This means it can handle everything from modernizing your Java applications or Node.js Lambda functions to shifting your Angular web apps to React-based stacks, upgrading libraries, or migrating legacy Windows/.NET and even mainframe or VMware workloads for a fully modern, AI-ready architecture (source, source, source).

Amazon positions Transform Custom as the first agentic AI service focused specifically on accelerating organization-specific software transformation, combining AWS-managed pre-built transformations for common upgrade scenarios plus the powerful ability to define and automate your own custom transformations expressed naturally in language tailored to your standards and environments (source, source).

Core Use Cases and Transformation Patterns

Transform Custom is built to address the repeatable, often mundane tasks in modernization that can consume 20–30% of developer capacity but are crucial to maintaining a healthy codebase (source, source).

Common AWS-Managed Transformations

• • Language and runtime upgrades:
    Upgrade your Java 8 applications to Java 17, update Node.js and Python runtimes including AWS Lambda functions, and align builds with modern standards effortlessly (source, source).

• • AWS SDK and service updates:
    Migrate from AWS SDK version 2 to 3 or apply other AWS service changes seamlessly across multiple languages (source).
  • Cloud modernization:
    Modernize Lambda functions’ dependencies and runtime versions, bringing your serverless workloads up to date (source).

Custom, Organization-Specific Patterns You Define

With natural language descriptions and sample docs, you can teach the AI your unique modernization needs:

• • Framework migrations such as Angular to React, Vue.js to React, or state management shifts like Redux → Zustand (source, source).

• • Library and dependency upgrades:
    Automate updates for libraries like Pandas 1.x to 2.x, NumPy, Hadoop/HBase/Hive libraries, or JavaScript utilities like Lodash (source).

• • Code refactoring and pattern modernization:
    Transition from print statements to logging frameworks, refactor string concatenation to Python f-strings, insert type hints, or embed security patterns and observability instrumentation (source).

• • Script and file-by-file translations:
    Convert AWS Cloud Development Kit (CDK) scripts to Terraform configurations, Terraform to CloudFormation, Excel models to Python notebooks, or Bash scripts to PowerShell (source).
  • Architecture and platform modernization:
    Shift from legacy Windows/.NET apps to containerized Linux environments or modern UI stacks; or orchestrate migrations from mainframes and VMware systems through comprehensive campaigns (source, source).

This powerful mix enables organizations to tackle technical debt at scale and accelerate modernization efforts up to 5 times faster than manual approaches, with some projects seeing up to an 80% reduction in execution time (source, source, source).

Key Capabilities of AWS Transform Custom

What makes Transform Custom uniquely powerful and exciting? Here’s a breakdown of its standout capabilities:

Natural Language–Driven Transformation Definition

Users can simply describe the transformation in natural language. For example:

“Migrate all Angular 16 projects to Angular 19, update routing, adjust build configs, and migrate deprecated APIs.”

You can provide documentation, sample before-and-after code, and coding guidelines. The agent then derives comprehensive transformation rules automatically, which you can iteratively refine via chat-style interactions or by directly editing (source, source).

Scalable, Consistent Transformation Execution

Transformations can be applied consistently across hundreds or thousands of repositories, services, or modules, ensuring uniform modernization across distributed teams and systems (source). The execution modes include:

• • Interactive: Developers visually inspect and review changes as the AI agent works.
  • Autonomous: Run one-line CLI commands or embed transformation jobs natively into CI/CD pipelines and automated workflows (source).

Continual Learning and Feedback Loops

After each transformation run, the AI agent collects knowledge items extracted from debug information, human corrections, code/build behavior, and review comments. Transformation owners can vet and promote these insights, enabling the system to improve accuracy and alignment with organizational conventions over time. This continuous learning loop helps maintain code quality and evolution standards (source).

Transformation Registry and Sharing

Once you finalize a transformation, publish it to the transformation registry within your AWS account. This registry serves as a central hub where teams can:

• • Discover,

• • Reuse, and
  • Refine

transformation definitions, all controlled via fine-grained IAM permissions for security and collaboration (source).

Scale and Performance

AWS customers report staggering performance benefits:

• • Up to 5× faster modernization compared to manual efforts.

• • Up to 80% reduction in execution time for specific transformation patterns.
  • Capable of handling codebases ranging in size from small services to sprawling monorepos with multiple modules (source).

Inside the Transformation Workflow

Amazon Transform Custom follows a clear four-phase workflow to streamline large-scale modernization efforts (source):

1. Define Transformation

• • Start your transformation definition via CLI (atx custom) or AWS web experience.

• • Provide a natural language description, attach documentation, design guidelines, and sample code (before/after examples).

• • The AI agent generates an initial transformation spec that you can refine interactively or through direct specification edits.
  • Skip this phase if using AWS-managed pre-built transformations (source).

2. Test and Validate

• • Run your transformation on sample or pilot repositories.

• • Inspect and review transformation results, build pipelines, and any discrepancies against your original intent.
  • Update your specs and feedback knowledge base accordingly.

3. Publish

• Publish your verified transformation to the transformation registry, giving it a clear name and objective. This allows your entire organization to discover and utilize the transformation going forward (source).

4. Execute at Scale

• • Run the published transformation on all target repositories or modules interactively or autonomously.
  • Track progress via the web-based campaign management console, which monitors execution status, handles errors, and manages retries (source).

CLI and Developer Experience

Amazon provides a dedicated CLI tool (atx) to support developers in creating, testing, and managing Transform Custom workflows (source).

– Launch an interactive session:

atx custom

This interactive environment guides you through creating, discovering, executing, and refining transformations. You can trust file read/write tools using the --trust-all-tools flag (-t).

– Typical commands include:

atx custom def save-draft         # Save your transformation definition draft  
atx custom def publish            # Publish the transformation to the registry  
atx custom discover              # List available transformations  
atx custom --help                # Show CLI help and options  

You can continue previous interactive sessions by restoring them with --conversation-id {id}, supporting flexible, long-running work (source).

For broad campaign execution, monitoring, and management, the AWS web console provides an intuitive UI for teams and managers (source).

Relationship to Other AWS Transform Capabilities

Transform Custom is one vital part of the AWS Transform landscape, which includes other, more specialized services:

• • Transform for .NET / Windows Modernization converts legacy ASP.NET Web Forms apps to modern Blazor on ASP.NET Core, enabling migrations from Windows/IIS to containerized Linux environments, complete with server-side Blazor components, real-time transformation status, and iterative refinements (source).
  • Legacy stack modernization features broad initiatives to migrate mainframe, VMware, and other legacy workloads to modern architectures, with Transform Custom serving as the core AI engine enabling highly customizable transformations aligned with organizational standards (source).

Transform Custom stands out as the customizable, flexible tool that lets organizations standardize and automate their unique modernization patterns rather than adopting one-size-fits-all scripts (source).

Impact on Technical Debt and Organizations

For decades, technical debt—the deferred cost of shortcuts or legacy technology constraints—has slowed innovation and inflated engineering workload. Amazon Transform Custom is a game-changer in this domain:

• • Systematic reduction in technical debt:
    By automating repetitive remediation, upgrades, and refactoring, Transform Custom prevents technical debt from compounding unchecked (source).

• • Massive productivity gains:
    Organizations report modernization efforts running up to 5× faster than manual approaches, while reducing execution time by as much as 80% across multiple patterns (source).

• • Consistent, high-quality transformations:
    Centralized, continuously refined transformation definitions ensure consistent adherence to corporate coding standards, reducing errors and variability especially in complex, distributed teams (source).
  • Empowered engineers:
    By offloading bulk mechanical edits to AI agents, developers can focus on higher-value work like innovative architecture, new features, performance optimization, and integrating AI capabilities into applications (source).

Industry analysts and customers alike herald Transform Custom as a vital tool to accelerate digital transformation journeys while controlling legacy risk in an increasingly AI-driven software ecosystem (source, source).

Where to Learn More

For readers eager to dive deeper into Amazon Transform Custom and start applying its power in their own environments, here are the best resources:

• • AWS Transform Custom User Guide:
    Detailed concepts, patterns, CLI examples, and full workflows

• • AWS Product Page:
    Overview, demo, videos, capabilities

• • AWS “What’s New” and Launch Announcements:
    Official release info and positioning

• • AWS Launch Blog “Crush Tech Debt with AI-Powered Code Modernization”:
    Practical insights and customer cases
• Analyst and Media Coverage:
  External perspectives on impact and strategy:
  – petri.com AWS agentic AI
  – Technology Magazine
  – InfoQ

Final Thoughts

Amazon Transform Custom heralds a new era where AI agents automate the heavy lifting of large-scale code modernization across any platform, language, or architecture. Its natural language interface, intelligent learning loop, and scalable execution promise to eliminate technical debt faster and more reliably than ever before.

For organizations managing sprawling legacy systems, battling script drift, or updating cloud-native applications, this capability offers a thrilling glimpse of the future — where your developers are freed from repetitive toil, and your modernization campaigns accelerate with AI-powered precision.

If your team’s modernization ambitions have felt daunting or slow so far, it’s time to discover the transformative potential of Amazon Transform Custom—the AI agent ready to help you modernize smarter, faster, and at unmatched scale.

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Frequently Asked Questions

• What programming languages does Amazon Transform Custom support?

  Amazon Transform Custom supports virtually any programming language, framework, runtime, or architecture, allowing extensive flexibility for large-scale modernizations.

• How does agentic AI improve the modernization process?

  Agentic AI autonomously generates, applies, and improves code transformations using natural language inputs and continuous learning, dramatically accelerating and scaling modernization efforts.

• Can I define my own custom transformation patterns?

  Yes. Transform Custom allows you to define and automate unique, organization-specific modernization tasks using natural language descriptions and sample documentation.

• Is there a way to test transformations before applying them broadly?

  Transform Custom supports testing and validation phases where you can apply transformations to sample or pilot repositories and review results before wide execution.

• How is security and collaboration handled for transformation definitions?

  Transformation definitions are managed in a centralized registry governed by fine-grained IAM permissions, allowing secure discovery, reuse, and refinement across teams.