{"id":4033,"date":"2025-09-03T09:27:22","date_gmt":"2025-09-03T17:27:22","guid":{"rendered":"https:\/\/vfunction.com\/?p=4033"},"modified":"2025-09-09T07:00:17","modified_gmt":"2025-09-09T15:00:17","slug":"ai-augmented-development","status":"publish","type":"post","link":"https:\/\/vfunction.com\/blog\/ai-augmented-development\/","title":{"rendered":"From Cool Vendor to critical capability: vFunction one year after Gartner\u00ae recognition"},"content":{"rendered":"\n

In August 2024, vFunction was named a Gartner\u00ae Cool Vendor<\/a> in AI-Augmented Development and Testing<\/strong>. The recognition validated our vision for modernizing application architecture\u2014providing deep visibility into an application\u2019s structure and proactively addressing architectural technical debt. This transforms monoliths into modular microservices-based apps that boost scalability and engineering velocity. One year later, with the rise of GenAI coding assistants like Amazon Q Developer and GitHub Copilot, this recognition feels even more relevant\u2014cementing architectural modernization as a critical enabler of AI-augmented development.<\/p>\n\n\n\n

AI is transforming how software gets built. It can generate code, suggest fixes, and even refactor functions. But AI works at the code level, not the system level. Without understanding runtime behavior, GenAI risks making complexity worse, unable to understand the logical domains and how to best modularize the code..<\/p>\n\n\n\n

The brownfield reality<\/h2>\n\n\n\n

This challenge is especially acute for brownfield applications. Unlike greenfield projects, which represent a small fraction of what enterprises run today, the vast majority of critical systems are brownfield<\/a>: large, complex applications with years of accumulated dependencies, complex flows, and architectural drift. Recent stats show that more than 60% of IT budgets<\/a> is spent simply maintaining these systems. These are the critical apps that drive revenue, support operations, and enable customer experiences.<\/p>\n\n\n\t\t

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At the extreme end are the \u201cmegaliths\u201d\u2014applications with over 20,000 classes and more than 10 million lines of code. <\/strong>These applications are so large and tangled that even incremental change feels daunting. Some teams have already lifted and shifted them to the cloud, but their monolithic nature prevents them from realizing the cloud benefits. Others are still in the process of migrating and face the same challenge.

At that scale, small inefficiencies multiply quickly, slowing engineering velocity and triggering resiliency issues. Simple containerization\u2014or relying on code assistants without architectural context\u2014can\u2019t address the complexity, modularize the code, or truly solve these problems.<\/p>\n\n\n\n

Why architectural context matters<\/h3>\n\n\n\n

That\u2019s why architectural context<\/strong> is essential. Without it, GenAI can\u2019t solve the challenge of modularization\u2014breaking monoliths into smaller, well-structured services. This is critical for taking full advantage of cloud-native services and reducing complexity and technical debt.

Without runtime-based architectural context, GenAI struggles to:<\/p>\n\n\n\n