Summary
AWS Transform uses agentic AI to accelerate legacy system modernization by analyzing code, extracting business logic, and generating cloud-native applications. This guide explains AWS Transform, Amazon Transform for Mainframe, modernization strategies, AI-powered workflows, key benefits, common use cases, and how organizations can modernize legacy applications with greater accuracy and confidence.
Mainframe systems that took decades to build now take years and millions of dollars to change. Worse, the specialists who understand them are retiring, and few people are being trained to replace them.
This guide breaks down what AWS Transform actually is, how agentic AI is closing that gap, and what real accuracy and safety look like when AI is doing the heavy lifting on a core banking or ERP system.
We start by understanding why this gap exists in the first place. Then we’ll walk through exactly how agentic AI is closing it, and what BIG’s Databricks and AWS Select Tier Partner and mainframe modernization experience add while we do it.
What Is AWS Transform?
Amazon Web Services Transform is an agentic AI service built to accelerate and automate the migration and modernization of legacy enterprise workloads. Instead of a team manually reading through decades-old code line by line, Amazon Web Services Transform analyzes legacy applications, extracts the business logic buried inside them, and generates modern, cloud-native code, compressing timelines that used to take years into months.
The core promise of AWS Transform is not to replace human judgment but to remove the bottleneck of having to reverse-engineer systems that nobody fully documented manually in the first place.
What Is AWS Transform for Mainframe?
AWS Transform for Mainframe is an agentic AI-powered service designed to accelerate the modernization of legacy mainframe applications. The mainframe-specific version of Amazon Transform narrows in on exactly the kind of systems most organizations are worried about touching, including COBOL, CICS, DB2, and VSAM-based applications running mission-critical workloads.
It reimagines these systems into cloud-native, AI-ready applications, with every generated output traceable back to the original source code.
That traceability matters more than it might sound. It means nothing gets generated in a vacuum; every piece of modern code Amazon Transform for mainframe produces can be traced back to the exact legacy logic it came from.
Key Features of AWS Transform
Before getting into how agentic AI actually powers the process, it’s worth knowing what AWS Transform is doing under the hood. These are the core capabilities that make the whole thing work:
- Automated code assessment and dependency mapping: Understand how a legacy system’s pieces connect before anything gets touched.
- Business rule extraction with full traceability: Pulls the logic embedded in old code and keeps it linked back to its source.
- Automated technical documentation: Generates the documentation that, in most legacy systems, was never written in the first place.
- Code decomposition into manageable domains: Breaks a monolithic system into pieces that are actually possible to work with.
- Integration with modern development tools: Connects through open protocols like MCP, so teams aren’t locked into a single toolchain.
How Agentic AI Powers Amazon Web Services Transform
This is where AWS Transform for mainframe actually differs from a typical AI coding assistant. It doesn’t just help a developer write code faster; it works through the entire modernization lifecycle largely on its own.
AWS powers enterprise transformation by deploying autonomous AI systems that break down complex workflows, make decisions, and execute tasks across the cloud. Rather than just generating content, agentic AI on AWS uses reasoning and APIs to automate operations from start to finish.
Discovery, Assessment & Environment Mapping
The autonomous AI agent initiates the process by scanning the legacy environment without requiring waiting on a manual review. Instead of manually mapping servers or legacy dependencies, the AI automatically scans on-premises environments, analyzes performance data, maps millions of business-application relationships, and determines optimal cloud targets.
Code Understanding & Business Rule Extraction
The AI agent parses through legacy programming languages (such as COBOL, CICS, DB2, VSAM, Java, and .NET) to decompose monoliths into manageable domains and microservices.
During this phase, the logic and business rules embedded deep within the undocumented old code are automatically identified, isolated, and extracted.
Cloud-Native Code Generation
Modern, cloud-native code is generated based on what was extracted. The AI agent decompiles the monolithic legacy architecture and converts the code into modern runtimes or containers.
Workload Orchestration & Parallel Execution
Runs alongside steps 1–3These steps run at the same time across multiple parts of the system, not one at a time. Rather than handling a massive system one piece at a time, the AI breaks down user requests into actionable steps and triggers external APIs without human intervention.
This enables the assessment, extraction, and generation steps to execute simultaneously and in parallel across multiple parts of the workload.
Deployment, Operations & Coexistence
Once fully validated and approved by human experts, the agent handles the target cloud deployment.
It deploys the infrastructure directly onto AWS (e.g., configuring Amazon ECS or EKS clusters and right-sizing EC2 instances) and can configure a hybrid coexistence model where the mainframe and modern systems run side by side via ongoing data replication during the transition phase.
Traceability built into every step
Each output stays linked back to its original source, so nothing gets lost or assumed along the way
That last point is the one worth sitting with. In a system this critical, an untraceable AI output isn't just unhelpful, it's a liability.
Amazon Web Services Transform Use Cases
Knowing what AWS Transform can technically do is one thing. Seeing where it actually gets used is what makes the value real. Here’s where organizations are putting it to work today:
Breaking apart a monolithic COBOL application into microservices
A single, tightly coupled COBOL system often means one small change risks breaking something unrelated. AWS Transform maps out the dependencies first, so teams can split the application into independent, manageable pieces, ones that can be updated, tested, and scaled on their own, without holding the entire system hostage to a single change.
Extracting decades of undocumented business logic before a system migration
Most legacy systems were never fully documented, which means the actual rules governing how a business runs, pricing logic, eligibility checks, and exception handling exist only inside the code itself. AWS Transform pulls that logic out and makes it visible before migration begins, so none of it gets silently lost or guessed at during the move.
Reducing reliance on a shrinking pool of mainframe-skilled talent
Every organization running COBOL is racing the same clock: fewer people every year who can read, understand, and safely change this code. AWS Transform takes on the heaviest analytical work automatically, the kind that would otherwise require a scarce, expensive specialist working manually for months, so the timeline no longer depends entirely on finding that one person.
Modernization Patterns AWS Transform Supports
Amazon Transform for mainframe isn’t a one-size-fits-all rebuild. It supports several recognized modernization patterns, and most organizations end up mixing more than one.
- Replatform: move the application with minimal changes, a faster, lower-risk starting point
- Refactor: restructure and update the code while preserving the original business logic
- Reimagine: extract the business rules and rebuild the application on a modern, cloud-native platform
- Hybrid coexistence: most organizations don’t move everything at once; mainframe and modernized systems typically run side by side during the transition, staying in sync through ongoing data replication, so the business keeps running smoothly instead of facing a single, high-risk cutover
How AWS Transform Ensures Accuracy You Can Trust
In mainframe environments, near-perfect accuracy is not good enough. A single misread business rule in a core banking or ERP system can cause real damage. Agentic AI is powerful, but large language models are inherently probabilistic, which is exactly why Amazon Web Services Transform doesn’t rely on AI judgment alone to guarantee correctness.
- Deterministic reverse-engineering: Analysis, dependency mapping, and business rule extraction run through deterministic, purpose-built tools combined with AI, not a probabilistic model interpreting code freely.
- Deterministic code conversion: For refactor projects, COBOL-to-Java conversion follows a rule-based, deterministic approach that preserves the original logic, not a generative rewrite.
- Functional equivalence testing: Automatically generated test plans, test data, and test scripts confirm the modernized application produces the same outputs as the original under the same conditions.
- Human-in-the-loop validation: Every AI-generated specification and piece of code is reviewed by application experts before moving to the next phase, and again before production deployment.
- Built-in regression testing: the same functional tests used at launch become the foundation for ongoing regression testing, protecting accuracy continuously, not just once at cutover.
Together, these are what actually make Transform’s output trustworthy for mission-critical systems: deterministic tooling wherever precision matters most, and human validation everywhere AI judgment is involved.
How Bits In Glass Helps You Get the Most from AWS Modernization
AWS Transform can do the heavy analytical lifting, but getting real value out of an AWS modernization initiative still takes the right delivery approach: planning, governance, and integration, not just turning the tool on.
Bits In Glass is an AWS Select Tier Services Partner with hands-on experience modernizing legacy systems and deploying AWS AI capabilities like Bedrock, SageMaker, and Amazon Q. Our approach starts with understanding the problem first, then walks you through every step of the journey, bringing a centralized, unified approach instead of leaving you with disconnected tools and half-finished migrations.
If you’re evaluating what an Amazon Transform initiative would actually take for your systems, talk to our team about where your mainframe modernization stands today.
Frequently Asked Questions
It is an agentic AI service that automates enterprise modernization, analyzing legacy code, extracting business logic, and generating cloud-native applications, compressing timelines that used to take years into months.
It works through the modernization lifecycle largely on its own, assessing legacy code, extracting business rules, and generating modern code in parallel across workloads, with traceability built into every step.
AWS modernization refers to updating legacy applications and infrastructure to run on modern, cloud-native AWS environments, often using tools like Amazon’s Transform to accelerate the process.
Yes. Amazon Transform for mainframe specifically reimagines COBOL, CICS, DB2, and VSAM-based applications into cloud-native, AI-ready systems, with every output traceable back to its original source.
