Enterprise AI is currently facing a massive spending problem. Companies are pouring billions into foundation models and infrastructure, yet 95% of these projects never make it out of the testing phase. The issue isn't the AI itself; the problem is how we try to force modern, probabilistic models to work with rigid, decades-old business systems. Most engineering teams rely on manual coding, and fragile API wrappers to connect the two. It is a cycle that drains budgets, creates blind spots, and breaks constantly.This paper takes a completely different approach. Instead of bolting a generic AI chatbot, or an AI agent, onto the outside of an application, Real-Time Discovery and (self) Coding (RTDC) engine embeds directly into your existing stack. It works as an autonomous digital workforce that actively scans an enterprise systems, understands the enterprise underlying business rules, and writes its own integration code on the spot. This function of an Application-Aware AI platform completely removes the need for manual data mapping, giving AI teams instantaneous enterprise integration with zero manual effort, and at a quasi-zero cost. RTDC integrations is designed for AI use cases, but it can also be used for traditional enterprise system integration situations. With RTDC, forward deployed engineering teams, can be significantly replaced, or complemented, with a forward deployed team of AI agent workers performing the tasks of RTDC for application-aware AI. Zenera product offering is an example of RTDC on application-aware agentic AI platform. There are other platforms that provide more limited variations of the idea.