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 Legacy systems, with their outdated technologies and rigid architectures, represent significant technical debt and maintenance burdens for organizations.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
Composable AI Agents for Intelligent Automation in Multi-Cloud Enterprise Environments: A Framework for Next-Generation Digital Transformation (Published)
This article presents a comprehensive framework for implementing composable AI agents across multi-cloud enterprise environments to enable next-generation digital transformation. As organizations increasingly adopt distributed cloud infrastructures, they face significant challenges in achieving seamless process automation across platform boundaries. Traditional robotic process automation approaches demonstrate initial value but frequently fail to scale in complex multi-cloud scenarios. Composable AI agents address these limitations by functioning as autonomous, containerized microservices that can be orchestrated to perform specialized tasks within broader business processes. The framework delineates the core principles of agent composability—autonomy, reusability, and composability—alongside a four-layer architectural model encompassing agent, integration, orchestration, and governance layers. Implementation patterns are categorized into architectural approaches (serverless, container-based, and managed endpoints), integration mechanisms, and orchestration strategies, with a detailed examination of enabling technologies. Through case studies in financial automation and IT service management, the article demonstrates how composable AI architectures accelerate digital transformation timelines, reduce manual intervention requirements, enhance process flexibility, and deliver substantial cost savings. The framework provides enterprises with a structured approach to implementing intelligent automation solutions that maintain effectiveness across diverse cloud environments while adapting to changing business requirements.
Keywords: Intelligent automation, autonomous agents, composable AI agents, enterprise digital transformation, microservices orchestration, multi-cloud architecture