Posts Tagged ‘Capgemini’
[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.
Links:
[DevoxxFR2025] Spark 4 and Iceberg: The New Standard for All Your Data Projects
The world of big data is constantly evolving, with new technologies emerging to address the challenges of managing and processing ever-increasing volumes of data. Apache Spark has long been a dominant force in big data processing, and its evolution continues with Spark 4. Complementing this is Apache Iceberg, a modern table format that is rapidly becoming the standard for managing data lakes. Pierre Andrieux from Capgemini and Houssem Chihoub from Databricks joined forces to demonstrate how the combination of Spark 4 and Iceberg is set to revolutionize data projects, offering improved performance, enhanced data management capabilities, and a more robust foundation for data lakes.
Spark 4: Boosting Performance and Data Lake Support
Pierre and Houssem highlighted the major new features and enhancements in Apache Spark 4. A key area of improvement is performance, with a new query engine and automatic query optimization designed to accelerate data processing workloads. Spark 4 also brings enhanced native support for data lakes, simplifying interactions with data stored in formats like Parquet and ORC on distributed file systems. This tighter integration improves efficiency and reduces the need for external connectors or complex configurations. The presentation showcased benchmarks or performance comparisons illustrating the gains achieved with Spark 4, particularly when working with large datasets in a data lake environment.
Apache Iceberg Demystified: A Next-Generation Table Format
Apache Iceberg addresses the limitations of traditional table formats used in data lakes. Houssem demystified Iceberg, explaining that it provides a layer of abstraction on top of data files, bringing database-like capabilities to data lakes. Key features of Iceberg include:
– Time Travel: The ability to query historical snapshots of a table, enabling reproducible reports and simplified data rollbacks.
– Schema Evolution: Support for safely evolving table schemas over time (e.g., adding, dropping, or renaming columns) without requiring costly data rewrites.
– Dynamic Partitioning: Iceberg automatically manages data partitioning, optimizing query performance based on query patterns without manual intervention.
– Atomic Commits: Ensures that changes to a table are atomic, providing reliability and consistency even in distributed environments.
These features solve many of the pain points associated with managing data lakes, such as schema management complexities, difficulty in handling updates and deletions, and lack of transactionality.
The Power of Combination: Spark 4 and Iceberg
The true power lies in combining the processing capabilities of Spark 4 with the data management features of Iceberg. Pierre and Houssem demonstrated through concrete use cases and practical demonstrations how this combination enables building modern data pipelines. They showed how Spark 4 can efficiently read from and write to Iceberg tables, leveraging Iceberg’s features like time travel for historical analysis or schema evolution for seamlessly integrating data with changing structures. The integration allows data engineers and data scientists to work with data lakes with greater ease, reliability, and performance, making this combination a compelling new standard for data projects. The talk covered best practices for implementing data pipelines with Spark 4 and Iceberg and discussed potential pitfalls to avoid, providing attendees with the knowledge to leverage these technologies effectively in their own data initiatives.
Links:
- Pierre Andrieux: https://www.linkedin.com/in/pierre-andrieux-33135810b/
- Houssem Chihoub: https://www.linkedin.com/in/houssemchihoub/
- Capgemini: https://www.capgemini.com/
- Databricks: https://www.databricks.com/
- Apache Spark: https://spark.apache.org/
- Apache Iceberg: https://iceberg.apache.org/
- Devoxx France LinkedIn: https://www.linkedin.com/company/devoxx-france/
- Devoxx France Bluesky: https://bsky.app/profile/devoxx.fr
- Devoxx France Website: https://www.devoxx.fr/
[DevoxxPL2022] Why is Everyone Laughing at JavaScript? Why All Are Wrong? • Michał Jawulski
At Devoxx Poland 2022, Michał Jawulski, a seasoned developer from Capgemini, delivered an engaging presentation that tackled the misconceptions surrounding JavaScript, a language often mocked through viral memes. Michał’s talk, rooted in his expertise and passion for software development, aimed to demystify JavaScript’s quirks, particularly its comparison and plus operator behaviors. By diving into the language’s official documentation, he provided clarity on why JavaScript behaves the way it does, challenging the audience to see beyond the humor and appreciate its logical underpinnings. His narrative approach not only educated but also invited developers to rethink their perceptions of JavaScript’s design.
Unraveling JavaScript’s Comparison Quirks
Michał began by addressing the infamous JavaScript memes that circulate online, often highlighting the language’s seemingly erratic comparison behaviors. He classified these memes into two primary categories: those related to comparison operators and those involving the plus sign operator. To understand these peculiarities, Michał turned to the ECMAScript specification, emphasizing that official documentation, though less accessible than resources like MDN, holds the key to JavaScript’s logic. He contrasted the ease of finding Java or C# documentation with the challenge of locating JavaScript’s official specification, which is often buried deep in search results and presented as a single, scroll-heavy page.
The core of Michał’s exploration was the distinction between JavaScript’s double equal (==) and triple equal (===) operators. He debunked the common interview response that the double equal operator ignores type checking. Instead, he explained that == does consider types but applies type coercion when they differ. For instance, when comparing null and undefined, == returns true due to their equivalence in this context. Similarly, when comparing non-numeric values, == attempts to convert them to numbers—true becomes 1, null becomes 0, and strings like "infinity" become the numeric Infinity. In contrast, the === operator is stricter, returning false if types differ, ensuring both type and value match. This systematic breakdown revealed that JavaScript’s comparison logic, while complex, is consistent and predictable when understood.
Decoding the Plus Operator’s Behavior
Beyond comparisons, Michał tackled the plus operator (+), which often fuels JavaScript memes due to its dual role in numeric addition and string concatenation. He explained that the plus operator first converts operands to primitive values. If either operand is a string, concatenation occurs; otherwise, both are converted to numbers for addition. For example, true + true results in 2, as both true values convert to 1. However, when an empty array ([]) is involved, it converts to an empty string (""), leading to concatenation results like [] + [] yielding "". Michał highlighted specific cases, such as [] + {} producing "[object Object]" in some environments, noting that certain behaviors, like those in Google Chrome, may vary due to implementation differences.
By walking through these examples, Michał demonstrated that JavaScript’s plus operator follows a clear algorithm, dispelling the notion of randomness. He argued that the humor in JavaScript memes stems from a lack of understanding of these rules. Developers who grasp the conversion logic can predict outcomes with confidence, turning seemingly bizarre results into logical conclusions. His analysis transformed the audience’s perspective, encouraging them to approach JavaScript with curiosity rather than skepticism.
Reframing JavaScript’s Reputation
Michał concluded by asserting that JavaScript’s quirks are not flaws but deliberate design choices rooted in its flexible type system. He urged developers to move beyond mocking the language and instead invest time in understanding its documentation. By doing so, they can harness JavaScript’s power effectively, especially in dynamic web applications. Michał’s talk was a call to action for developers to embrace JavaScript’s logic, fostering a deeper appreciation for its role in modern development. His personal touch—sharing his role at Capgemini and his passion for the English Premier League—added warmth to the technical discourse, making the session both informative and relatable.