How GenAI Agents Are Transforming Legacy Application Modernization (Published)
This article explores how Generative AI (GenAI) is revolutionizing legacy application modernization in enterprise environments. Legacy systems, with their outdated technologies and rigid architectures, represent significant technical debt and maintenance burdens for organizations. GenAI-powered agents are emerging as transformative tools that can analyze complex codebases, discover implicit knowledge, recommend customized modernization strategies, and automate code transformation. The article examines core capabilities of these AI agents, including automated code analysis, intelligent strategy formulation, code transformation, and API generation. It presents implementation approaches across assessment, execution, and governance phases, supported by case studies from financial services, healthcare, and manufacturing sectors that demonstrate substantial improvements in modernization speed, cost, and outcomes. As these technologies continue to evolve, they promise to fundamentally reimagine how organizations approach technical debt and enable more adaptive, innovative technology landscapes
Keywords: Legacy modernization, autonomous agents, code transformation, generative AI, technical debt
Data Quality, Feature Engineering, and Model Reliability in Large-Scale AI Multi-Agentic Systems (Published)
Large-scale artificial intelligence (AI) systems increasingly operate not as a single monolithic model but as a population of interacting, specialized agents separate models or decision-making components responsible for functions such as pricing, fraud detection, ranking, routing, and customer support that share underlying data infrastructure and, in many cases, influence one another’s inputs and outputs. This article synthesizes peer-reviewed literature on data quality assurance, feature engineering, and model reliability to examine how these three concerns interact once an AI system is decomposed into multiple cooperating agents operating at scale. Seventeen primary sources are reviewed, spanning foundational work on technical debt in machine learning systems, multi-dimensional data quality frameworks, scalable and automated data quality verification, data lifecycle management, empirically grounded data-management taxonomies, production-readiness testing rubrics, scalable and automated feature engineering, hyperparameter optimization, organizational workflow studies, a production-scale ML platform, concept drift, large-scale academic surveys of ML testing, systematic reviews of industrial ML challenges, and fault-tolerant cooperative control of multi-agent systems. Drawing on this literature, the article proposes a conceptual framework linking data quality assurance, feature engineering, model training and reliability testing, and deployment to an agent population, closed by a cross-agent monitoring and feedback loop. The review finds that data quality problems and model reliability failures do not remain confined to the agent in which they originate: because agents in a large-scale AI system typically share upstream data sources, feature pipelines, or downstream state, a defect introduced at the data or feature layer of one agent can propagate through the interactions between agents, producing system-level reliability failures that are not visible from the perspective of any single agent’s test suite. Ensuring reliability in such systems therefore requires treating data quality, feature engineering, and testing as cross-cutting, system-wide concerns rather than as properties to be verified independently within each agent.
Keywords: ML testing, concept drift, data quality, fault-tolerance, feature engineering, large-scale AI, model reliability, multi-agent systems, technical debt