Posts Tagged ‘Modernization’
[AWSReInvent2025] Accelerating Enterprise Modernization: The Architecture of Composable AI Agents
Lecturer
Mortaza Chowri is the Head of Product Management for the AWS Transform team, where he leads the development of next-generation tools for complex workload migration. He is an expert in leveraging generative AI to automate technical debt reduction for large-scale enterprises. Joining him are Alexi and Ravi, who serve as senior architects within the AWS Transform division, specializing in agentic AI implementation and the creation of composable system frameworks. The session also features strategic insights from the leadership team at Capgemini, who collaborate with AWS to deliver industry-specific modernization solutions for global banking and automotive clients.
Abstract
Enterprise modernization is frequently paralyzed by the extreme complexity of legacy systems, particularly decades-old mainframes and aging Windows-bound .NET applications. This article explores the innovative framework of AWS Transform, a centralized service that utilizes “Agentic AI” to automate and streamline the migration process. The methodology centers on the concept of composability, which allows AWS partners to integrate their proprietary industry knowledge and specialized tools with foundational AI agents. By utilizing a sophisticated chat-based interface and automated business rule extraction, the platform enables a seamless transition from legacy COBOL and .NET Framework 4.x to modern, cloud-native architectures. The analysis demonstrates how these composable agents create a continuous feedback loop that significantly reduces manual effort, improves documentation, and ensures business logic remains intact during high-risk migrations.
Context: The Burden of Technical Debt and Knowledge Atrophy
Many of the world’s most critical systems, particularly in finance and manufacturing, are still dependent on infrastructure built in the late 20th century. These legacy environments present three primary obstacles that prevent organizations from achieving modern agility. First, knowledge atrophy has become a critical risk, as the original architects of these mainframe systems have often retired, leaving behind “black box” applications that lack contemporary documentation. Second, the technical debt associated with older languages like COBOL is immense, as these systems were never designed to leverage modern cloud features such as serverless compute or elastic auto-scaling.
Third, the mission-critical nature of these systems creates a state of risk aversion, where the fear of breaking a core business process during a manual rewrite often leads to stagnation. AWS Transform was specifically developed to break this cycle of inertia. By providing a unified experience that integrates discovery, assessment, and modernization into a single platform, AWS allows enterprises to view their legacy code as an asset to be reimagined rather than a liability to be feared.
Methodology: Agentic AI and the Composable Framework
The core technical innovation of AWS Transform is the transition from static point solutions to a dynamic, “unified experience” powered by specialized AI agents. These agents are designed to perform complex technical tasks with a level of autonomy that far exceeds traditional automation scripts. The methodology is built upon several key pillars of agentic behavior. Discovery agents are tasked with automatically mapping technical artifacts, such as physical servers and complex database schemas, to their optimal cloud-native equivalents.
Modernization agents, specifically those tuned for mainframe environments, perform the difficult work of extracting business rules from legacy code. This process generates comprehensive documentation that allows current engineers to “comprehend” the underlying logic of systems they did not build. The most transformative aspect of this methodology is its composability for partners. AWS provides the foundational intelligence and large language models, while partners such as Capgemini can “compose” these with their own specialized knowledge bases and custom transformation rules. This enables the creation of industry-specific agents, such as a modernization assistant specifically optimized for banking regulations or complex automotive production logic.
Technical Analysis of Mainframe Rule Extraction
The implementation of these agents in real-world scenarios, particularly through the collaboration with Capgemini, highlights a sophisticated “forward engineering” approach. In this workflow, the AI agents first scan the legacy code to identify core business logic and immutable rules. This extraction phase is critical because it ensures that while the code is updated, the essential business functions remain perfectly intact. Following extraction, the reimagination phase begins, where these rules are integrated into a modern architecture that meets cloud-native standards for security and performance.
Practitioners interact with these systems through a chat experience within the AWS Transform interface, allowing them to query both the AI agents and integrated domain experts directly. This interaction model democratizes the modernization process, making it accessible to developers who may not have expertise in COBOL but are proficient in modern languages like Java or Python. The platform serves as a bridge, translating the “what” of legacy business logic into the “how” of modern cloud execution.
Outcomes: Efficiency, Consistency, and Continuous Learning
The deployment of composable AI agents has fundamentally altered the economics and speed of enterprise modernization. By automating the most labor-intensive parts of code comprehension and translation, organizations have reported a reduction in manual effort by as much as 80%. This allows teams to focus on high-value innovation rather than the repetitive task of line-by-line code migration. Furthermore, the platform ensures architectural consistency across a large organization, preventing the fragmentation that often occurs when different teams use varying migration tools.
One of the most significant consequences of this approach is the continuous improvement of the agents themselves. Every modernization task performed through the platform provides feedback data that enhances the underlying AI models. As these agents encounter more diverse enterprise environments, their ability to handle edge cases and complex business rules grows exponentially. This creates a virtuous cycle where each successful migration makes the next one faster and more reliable, effectively solving the problem of knowledge atrophy for the long term.
Conclusion
The shift toward agentic AI and composable architectures represents a milestone in the evolution of enterprise IT. AWS Transform provides a robust framework that allows organizations to tackle their most daunting legacy challenges with a level of confidence and speed that was previously impossible. By allowing partners to integrate their unique industry expertise into a centralized AI system, AWS has created a scalable ecosystem that transforms modernization from a risky, multi-year endeavor into a manageable and continuous strategic process.