The Era of Application-Aware AI

Escaping Pilot Purgatory with Real-Time Discovery & Coding (RTDC). Enterprise Intelligence, Instantly! Despite an estimated annual capital allocation of thirty to forty billion dollars toward Generative Artificial Intelligence (GenAI), enterprise adoption remains severely constrained by the Deployment Paradox. Current industry data indicates that ninety-five percent of enterprise pilot projects fail to graduate to production environments. This failure rate is fundamentally a failure of integration architecture rather than an inherent limitation of language models. Early enterprise deployments have relied on attaching generic conversational agents to the periphery of legacy software ecosystems. This model-level integration approach introduces substantial friction, lacks contextual awareness, and forces engineering teams into the Stitching Trap, i.e., the manual construction of highly brittle application programming interface wrappers across poorly documented legacy environments. This paper introduces the concept of Application-Aware AI, a novel architectural paradigm. Driven by a framework defined as Real-Time Discovery and Coding (RTDC), this approach operates as an autonomous entity that proactively discovers system logic, infers database schemas, and self-codes, under constraints, functional integrations dynamically based on user intent. The system executes a continuous four-layer loop encompassing total enterprise introspection, deterministic constraint enforcement, autonomous meta-agent orchestration, and dynamic user interface generation. By abstracting probabilistic language models behind a strict Model of Constraints, and transforms, i.e., ~skills, and logging all decisions within a highly transparent Reasoning Graph, the proposed paradigm resolves the liability of model hallucination. This design ensures complete regulatory auditability, facilitates the progressive modernization of legacy enterprise applications, like ERP and ITSM, via the Strangler Fig pattern, and allows organizations to establish a production-ready intelligence factory instantly.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-11
DOI
https://doi.org/10.5281/zenodo.19322701
Primary Topic
AI-based Problem Solving and Planning
Type
article
Field-Weighted Citation Impact
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article

The Era of Application-Aware AI

Stéphane Maes
Zenodo (CERN European Organization for Nuclear Research)
AI-based Problem Solving and Planning
article

The Era of Application-Aware AI

Stéphane Maes
article en

Abstract

Escaping Pilot Purgatory with Real-Time Discovery & Coding (RTDC). Enterprise Intelligence, Instantly! Despite an estimated annual capital allocation of thirty to forty billion dollars toward Generative Artificial Intelligence (GenAI), enterprise adoption remains severely constrained by the Deployment Paradox. Current industry data indicates that ninety-five percent of enterprise pilot projects fail to graduate to production environments. This failure rate is fundamentally a failure of integration architecture rather than an inherent limitation of language models. Early enterprise deployments have relied on attaching generic conversational agents to the periphery of legacy software ecosystems. This model-level integration approach introduces substantial friction, lacks contextual awareness, and forces engineering teams into the Stitching Trap, i.e., the manual construction of highly brittle application programming interface wrappers across poorly documented legacy environments. This paper introduces the concept of Application-Aware AI, a novel architectural paradigm. Driven by a framework defined as Real-Time Discovery and Coding (RTDC), this approach operates as an autonomous entity that proactively discovers system logic, infers database schemas, and self-codes, under constraints, functional integrations dynamically based on user intent. The system executes a continuous four-layer loop encompassing total enterprise introspection, deterministic constraint enforcement, autonomous meta-agent orchestration, and dynamic user interface generation. By abstracting probabilistic language models behind a strict Model of Constraints, and transforms, i.e., ~skills, and logging all decisions within a highly transparent Reasoning Graph, the proposed paradigm resolves the liability of model hallucination. This design ensures complete regulatory auditability, facilitates the progressive modernization of legacy enterprise applications, like ERP and ITSM, via the Strangler Fig pattern, and allows organizations to establish a production-ready intelligence factory instantly.

Zenodo (CERN European Organization for Nuclear Research)
Escola Superior de Saúde Egas Moniz (PT)
Industry, innovation and infrastructure
Openalex Percentile: Top 2%
AI-based Problem Solving and Planning
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The Era of Application-Aware AI — Stéphane Maes · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS