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AWS Reimagine: Does Agentic AI Finally Transform Mainframe Lift-and-Shift?
AWS expands its agentic AI mainframe service with deep architectural transformation and automated testing, shifting the focus from migration speed to achieving true cloud-native outcomes.
2/12/2025
Key Highlights:
The new Reimagine capability targets complete application rearchitecture, moving beyond mere code conversion to generate cloud-native microservices.
AWS is using agentic AI to analyze data lineage and generate automated data dictionaries, solving the critical knowledge gap problem inherent to legacy codebases.
New automated testing features, including test plan generation and data collection scripts, are designed to significantly derisk complex migration projects.
This enhancement directly challenges the market's traditional "lift-and-shift" approach by making more strategic transformations faster and less risky.
The offering leverages a "human in the loop" validation model, which ensures that technical automation preserves decades of critical business logic.
The News
Amazon Web Services has announced significant enhancements to its AWS Transform for Mainframe service, an agentic artificial intelligence solution designed to accelerate legacy system modernization. The update introduces the Reimagine modernization pattern, which focuses on full application rearchitecture, transforming monolithic mainframe systems into cloud-native microservices. Additionally, AWS launched automated testing functionality, aiming to streamline functional equivalence validation and reduce overall project timelines. This dual-pronged release addresses the primary barriers to deep modernization: the complexity of architectural change and the intensive labor required for testing. Find out more by clicking here to read the announcement blog.
Analyst Take
When AWS first introduced AWS Transform, my immediate thought was that we were watching the mainframe migration market reach a critical inflection point where scale and speed would fundamentally change the vendor landscape. The subsequent release of Reimagine and automated testing solidifies this view. This is not simply a product update. It’s a statement of intent by AWS. This intent was encapsulated by the dramatic stunt that AWS staged on day one of re:Invent when it literally dropped a server rack and blew it up!
This announcement by AWS represents a subtle but profound shift in how hyperscalers approach mainframe "exits." They are moving the goalposts from transactional refactoring, COBOL to Java, to true architectural transformation. The objective is now less about getting code off the mainframe and more about ensuring that the migrated application is genuinely cloud-native when it lands on AWS.
For decades, the standard playbook for CIOs facing legacy system debt included re-platforming, re-hosting, or, if they were ambitious, refactoring. These projects were a high wire act for CIOs where failure or at best cost overruns were the norm. These options occasionally delivered incremental cost savings but rarely achieved the availability agility and elasticity promised by the cloud. The Reimagine pattern, if you believe the AWS hype, directly targets this gap. It's a recognition that simply transforming spaghetti code into a different language is insufficient if the resulting application retains the original, monolithic structure.
I find the details around data analysis and automated documentation particularly fascinating. The biggest bottleneck in any complex modernization project is always institutional knowledge loss. As mainframe subject matter experts retire, the internal workings of mission-critical applications can become black boxes. AWS Transform is architected to address this talent and knowledge gap head-on. By combining enhanced business logic extraction with automated data dictionary generation and data lineage analysis, the service essentially reverse-engineers the application's intellectual property. This capability aims to give developers and architects the clarity they need to confidently redesign data architectures, thereby preserving core business logic during the transition to a distributed microservices environment. You cannot modernize what you do not understand. This tool allegedly solves for this.
What was Announced
The core of the announcement rests on the new Reimagine pattern and comprehensive Automated Testing capabilities. The Reimagine methodology follows a three-phase approach, Reverse Engineering, Forward Engineering, and Deploy and Test to guide the transformation from monolithic COBOL or JCL applications to cloud-native architectures.
Reverse engineering is designed to extract deep business logic and complex rules from the legacy source code. This phase uses AI-powered analysis that combines system analysis with organizational knowledge. The result is detailed technical documentation, including an automated data dictionary generation and full data lineage analysis. This advanced analysis is key; it aims to define the structure, meaning, usage, and relationships of the mainframe data to inform the new design.
The Forward Engineering phase is architected to utilize the extracted business logic to generate a microservice specification. It then generates modernized source code, along with the necessary Infrastructure as Code (IaC) templates and a modernized database schema. This phase primarily converts legacy code into modern languages such as Java, but crucially, it breaks down the monolith into loosely coupled components, delivering a microservices architecture. The service is designed to support the principle of human-in-the-loop validation, meaning domain experts are required to continuously validate the AI-generated specifications and code artifacts, minimizing transformation risk.
The new Automated Testing functionality is a huge derisking factor. It supports the IBM z/OS mainframe batch application stack at launch. The functionality includes planning, data collection, and automation scripts. The service is designed to automatically generate detailed test plans based on analyzing the mainframe code, business logic, and scheduler plans. It then generates test data collection scripts, specifically JCL scripts, to extract required test data from mainframe sources like VSAM files or DB2 databases for use in the target AWS environment. Finally, it generates execution scripts aimed at automating the functional testing of the modernized application, helping customers validate data migration, results validation, and terminal connectivity. This automation is necessary for velocity.
This release fits perfectly within the wider market trend.The consensusfor many analysts who track this space suggests that by 2025, over 85% of enterprises are embedding AI into their modernization efforts. I am personbally seeing this from vendors focused on getting clients off the mainframe, as well as vendors focused on modernizing applications on the mainfram. AWS is capitalizing on this acceleration. Critically, AWS is making these core features, including assessment and transformation, available to customers at no cost initially. This is a classic hyperscaler land-and-expand strategy, removing the initial budget barrier to adoption and ensuring customer applications land in their ecosystem. It is a shrewd move.
This move creates a stark competitive contrast with IBM and its ecosystem. IBM's counter-narrative, reinforced by advances like z/OS 3.2 and watsonx Code Assistant for Z, focuses on hybrid retention. IBM is arguing for keeping the core workload on the mainframe while exposing data and services via APIs for integration with cloud systems. AWS's strategy, however, is a full-scale cloud migration exit, positioning the mainframe as a cost sink that must be fully remediated and decommissioned. AWS Transform gives organizations the technical means to execute that end-to-end cloud strategy with unprecedented speed and confidence.
Looking Ahead
AWS Transform's push toward the Reimagine pattern significantly elevates the strategic importance of AI-driven transformation within the industry. The market is experiencing a tectonic shift, where the value proposition is no longer the destination, the cloud, but the methodology of the journey. The complexity of legacy systems previously mandated slow, custom, and exorbitantly expensive transformation projects. By productizing the deconstruction of the monolith through agentic AI, AWS is accelerating the commoditization of the initial stages of modernization.
As I said on my recap video from re:Invent, workloads are like journeys, while the car (or Uber) exists doesn’t mean all journeys should be taken by car. There are scenarios where boats, planes and trains are the right choice. Two truths can be true at the same time. Certain workloads are suited to the mainframe, and some aren’t. The mainframe can be growing MIPS-wise, and workloads can be candidates to migrate to the cloud. Enterprises need to take a balanced view on workload placement and focus on non-functional requirements such as security, availability, performance and sovereignty.
The key trend that I am going to be looking out for is the adoption rate of the Reimagine capability versus simple Refactor. For organizations with high technical debt and a clear business mandate for agility, Reimagine is the superior architectural outcome. However, it is also inherently riskier, even with automated testing. AWS is attempting to make the hardest modernization path the easiest to execute.
The announcement places direct competitive pressure on traditional systems integrators and niche modernization vendors. The hyperscalers, AWS, and to a lesser extent, Microsoft Azure and Google Cloud, which rely heavily on partners like Capgemini and Deloitte's innoWake suite—are now embedding the core technical heavy lifting into their platforms. This forces competitors to focus their value proposition higher up the stack, concentrating on organizational change management, industry-specific business process re-engineering, and strategic alignment, rather than simply writing code conversion scripts.
My perspective is that while the new testing features provide the necessary reassurance, the Reimagine pattern's success will ultimately be measured by the stability and maintainability of the generated microservices over time. Going forward, I am going to be closely monitoring how the company performs on customer case studies involving core systems that have successfully gone from COBOL batch processing to real-time Java microservices using this new pattern in future quarters. My ultimate measure of ‘success’ is that the mainframe was ultimately decommissioned, as this is when real cost savings are realized. That success will determine if AWS can truly claim the high ground in the increasingly crowded mainframe modernization race.
Steven Dickens | CEO HyperFRAME Research
Regarded as a luminary at the intersection of technology and business transformation, Steven Dickens is the CEO and Principal Analyst at HyperFRAME Research.
Ranked consistently among the Top 10 Analysts by AR Insights and a contributor to Forbes, Steven's expert perspectives are sought after by tier one media outlets such as The Wall Street Journal and CNBC, and he is a regular on TV networks including the Schwab Network and Bloomberg.