How GenAI and Agentic AI are redefining application modernisation

The age of AI and digital transformation is well underway, yet a significant proportion of corporate core enterprise systems continue to rely on ageing technologies such as COBOL, PL/SQL, C++, and old Java frameworks. These systems have reliably supported business operations for decades, but their complexity, rigidity, and high maintenance costs increasingly hinder a company's ability to evolve. In today's competitive environment, where innovation cycles are shorter and customer expectations are higher, legacy platforms represent not only a technical challenge but a business constraint.

CIOs and IT leaders recognise that without modern, flexible, cloud-ready architectures and applications, organisations risk losing agility, resilience, and market share. Despite this, and even though the pressure to modernise these systems has never been stronger, traditional approaches to modernisation, such as lift and shift to new infrastructure, automatic code conversion and code rewrite from scratch, can be prohibitively expensive, lenghty and inherently risky, making many organisations reluctant to embark on them.

This is where Generative AI (GenAI) and Agentic AI are reshaping the landscape. Their emergence marks a genuine turning point, offering new, more efficient ways to rethink and revitalise legacy applications. When applied strategically, GenAI goes far beyond mere code translation: it can re-engineer entire architectures, drastically reducing both time-to-delivery and project cost. Meanwhile, Agentic AI extends these capabilities by orchestrating autonomous workflows across the entire software development lifecycle, helping teams modernise with unprecedented speed and quality. 
GenAI and Agentic AI have thus introduced a new approach that enables a more comprehensive modernisation of legacy applications. This new approach rapidly converts languages, reduces operational errors, redesigns architectures based on new technology stacks, automatically generates functional and technical documentation, eliminates manual activities, and tests the application with an iterative improvement approach. Through a semi-automated process, generative and agentic AI fully transform applications, bridging the gap between innovation and operational continuity, delivering efficiency gains of between 30% and 60% compared to traditional models, depending on application complexity.

However, despite their transformative potential, GenAI and Agentic AI should not be seen as magic solutions. Their success depends on an orchestrated, methodological approach combining specialised expertise, advanced tooling, and solid governance. Modernisation projects based on GenAI and Agentic AI achieve the best results when they follow a clearly defined methodological path, structured into four key macro-phases:

• Assessment: detailed analysis of the source application, component classification, code cleansing, and definition of the target architecture.
• Prompt design: creation of prompts and pipelines that guide GenAI in transforming the application, establishing rules and technical standards.
• Iterative modernisation cycles: the code is transformed through successive cycles, with checks and optimisations after each iteration to progressively refine the result.
• Testing and consolidation: technical, functional, and user acceptance tests ensure that the modernised application meets quality, security, and performance requirements before going into production.

To support this methodology, it is also essential to deploy dedicated agents that automate the various phases of the process, from source code analysis and clustering to file preparation, prompt and transformation rule design, new code generation, and KPI monitoring on output quality. All of this is managed by an engine that activates and orchestrates the individual agents performing the modernisation tasks.

Moreover, to best tackle modernization projects, AI must be integrated and governed with the support of experienced professionals who understand both legacy systems and modern architectures. Their supervision ensures that outputs meet enterprise-grade quality standards and that strategic decisions align with long-term business goals.

GenAI and Agentic AI are not simply speeding up old processes, they are enabling a new paradigm in application modernisation, making it possible to deliver in a matter of months what previously required years, bridging the divide between innovation and operational stability. For organisations seeking to unlock innovation, scalability, performance, and agility, the message is clear: the future of legacy transformation will be AI-driven.