Traditional software engineering methods rely heavily on time-consuming manual tasks, often leading to technical debt due to dependencies on individual expertise and institutional knowledge. It is difficult to ensure efficiency, reliability, and scalability across the software
development life cycle; these challenges are compounded later in the software life cycle when budget and resource limitations also come into play. Despite the advancements in Generative AI-powered platforms, there is a decline in productivity when managing complex projects that
entail interdependencies among applications, a common scenario in complex solutions.
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