AWS Transform Simplifies Legacy Modernization with Agentic AI

AWS Transform

AWS Transform is a modernization automation service that uses agentic AI to analyze, refactor, and migrate legacy workloads to the AWS Cloud. Instead of spending months manually discovering dependencies, rewriting code, or planning migrations, enterprises can now automate much of this process.

The platform supports modernization across multiple environments, including:

• Mainframes: Analyze COBOL or PL/I applications and migrate them to modern Java-based architectures.
• .NET Applications: Refactor Windows-dependent applications into cloud-native, cross-platform versions.
• VMware Workloads: Automate discovery, dependency mapping, and migration planning for VMware environments.

With AWS Transform, modernization projects that once took years can now be completed in months instead of years.

Why AWS Transform Matters

Modernizing legacy systems is not just a technical upgrade, it’s a strategic business decision. Here’s why AWS Transform is a game-changer:

1. Speed and Efficiency

Traditional modernization involves lengthy manual analysis and complex planning. AWS Transform uses AI agents to automatically discover dependencies, generate migration plans, and refactor code. AWS reports that network conversion for VMware migrations can be up to 80× faster with Transform than with manual methods.

2. Lower Risk and Cost

Every legacy migration carries risks, unknown dependencies, downtime, or broken integrations. By automating discovery and refactoring, AWS Transform reduces human error and ensures dependencies are fully mapped before migration, thereby lowering both project risk and overall cost.

3. Business Agility

Once legacy systems are modernized, organizations can adopt cloud-native architectures, integrate AI and analytics, and deploy new features faster. It’s not just about technology, it’s about enabling innovation and accelerating time-to-market.

Key Capabilities of AWS Transform

AWS Transform combines automation, AI, and human oversight to streamline modernization end-to-end. Its core features include:

• Automated Discovery: Scans legacy environments (like VMware or mainframes) to identify applications, interdependencies, and configurations.
• AI-Driven Refactoring: Uses machine learning models to convert legacy code, such as COBOL, into modern languages like Java while preserving business logic.
• Migration Planning: Automatically generates migration “waves” and network mappings to guide execution.
• Human-in-the-Loop: While AI handles most of the heavy lifting, experts can review, validate, and approve critical changes.
• End-to-End Workflow: Supports all stages, from discovery and analysis to refactoring and migration execution.

Together, these features make AWS Transform a comprehensive modernization platform rather than just another migration tool.

Use Cases

1. VMware Migration

Many enterprises are reconsidering VMware due to rising costs and licensing changes. AWS Transform can automatically detect all virtual machines, dependencies, and networks, then generate optimized AWS migration plans. This helps companies transition to EC2-based environments faster and more cost-effectively.

2. Mainframe Modernization

For organizations running COBOL-based mainframes, AWS Transform analyzes and decomposes monolithic codebases, then automatically refactors them into Java microservices that run on AWS. This enables a smooth path from legacy to modern cloud architectures without losing business logic.

Best Practices for Adopting AWS Transform

To get the most value from AWS Transform, organizations should:

1. Start with a Pilot Project: Select a small but representative workload to evaluate automation accuracy and performance.
2. Define Clear Goals: Identify whether modernization aims at cost savings, agility, or innovation.
3. Involve Stakeholders Early: Collaboration between IT, business, and operations teams is essential for success.
4. Combine AI with Expertise: Use AI automation for speed, but ensure experts review and validate results.
5. Measure Business Impact: Track KPIs like migration time, cost savings, and performance improvements.

Challenges to Keep in Mind

While AWS Transform simplifies modernization, it’s not a magic solution. Some challenges still require attention:

• Complex Business Logic: AI can refactor code, but domain experts must validate accuracy.
• Data Migration: Transform focuses on applications, data migration must be handled separately.
• Change Management: Modernization often demands new workflows, cloud skills, and cultural adaptation.

Understanding these factors helps in planning realistic timelines and expectations.

Why Now?

The timing for AWS Transform couldn’t be better. Enterprises worldwide face end-of-support deadlines, rising licensing costs, and pressure to modernize. At the same time, the rise of Generative AI and agentic automation has made AI-driven modernization both achievable and cost-effective.

AWS Transform brings these elements together, combining AI automation, proven AWS cloud infrastructure, and human oversight to deliver faster modernization with less risk.

Conclusion

AWS Transform represents the next evolution in cloud modernization. By leveraging AI to analyze, refactor, and migrate legacy workloads, it drastically reduces complexity, accelerates timelines, and minimizes risk.

Drop a query if you have any questions regarding AWS Transform and we will get back to you quickly.

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