Legacy Modernization: The New Era of Software Development

At "Universe 2025", GitHub presented AgentHQ, a groundbreaking platform that serves as a central control center for all AI-supported coding agents. For the first time, AgentHQ combines the control, monitoring, and integration of various AI agents in a unified, secure environment. This new platform enables companies to gain control over their AI-supported software development, standardize processes, and take collaboration between humans and AI to a new level. These innovations in recent months mark the beginning of a new era in software development, where agentic systems not only support but also actively and comprehensively participate in the development process.

This revolution in software development will lead to significantly greater efficiency in the future, as software developers can have several features developed in parallel by different agents. A real milestone, but also a profound change in the way software will be developed in the future.

But how does this development contribute to replacing legacy systems? The new AgentHQ is specifically designed for the development of new features or adaptations to existing code. However, the old systems still run on outdated programming languages, such as COBOL.

Replacement by new building

One conceivable approach would be that, with the help of the new technical possibilities, a new build to replace the legacy application can be implemented much more efficiently and quickly than before. Despite this progress, however, considerable risks remain. Replacing large and complex legacy systems in particular remains a challenging undertaking that often takes several years. The challenges, such as ensuring data integrity, integrating existing business processes and migrating historically grown interfaces, remain even with modern tools. There is also a risk of critical functions being lost or unexpected problems occurring during the changeover.

Legacy modernization is followed by transformation

Another alternative when dealing with legacy applications can be the actual transformation of the existing application. The starting point for such a transformation is usually a modernization. This involves transferring the old code of the legacy application into a modern programming language, such as Python, with as little loss of structure as possible. By using AI-supported automation, this step of modernization is associated with comparatively less risk, as the basic functionality is retained.

Through modernization, many of the typical problems associated with legacy systems, such as the lack of qualified specialists for outdated technologies or high maintenance costs, are effectively addressed. After this step, the company has a solid foundation.

This is precisely where the advantages of new platforms and AI-supported development tools come into play. With the help of AI agents working in parallel, it is possible to further develop the modernized application in a targeted manner, for example, transforming it from a purely batch-oriented application into an online-capable, interactive one. The automation and parallel processing by AI agents ensures that this transformation can be implemented much more efficiently and quickly than with conventional methods.

And what happens if companies ignore the new possibilities and continue to rely on outdated systems? This is not entirely foreseeable; however, it is likely that the potential offered by the new possibilities in software development cannot be fully exploited for outdated legacy systems. In addition to the already high costs of legacy systems, there would also be a lack of efficiency gains.