For the last several years, the dominant narrative in "agentic AI" has been that large language models should orchestrate information access by dynamically selecting tools, issuing sub-queries, and synthesizing results. We argue this approach is misguided: enterprises do not suffer from a reasoning deficit, but from a data integration problem. Enterprises are data-centric: critical information is scattered across heterogeneous systems (e.g., databases, documents, and external services), each with its own query language, schema, access controls, and performance constraints. In contrast, contemporary LLM-based architectures are optimized for reasoning over unstructured text and treat enterprise systems as either corpora or external tools invoked by a black-box component. This creates a mismatch between schema-rich, governed, performance-critical data systems and text-centric, probabilistic LLM architectures, leading to limited transparency, weak correctness guarantees, and unpredictable performance. In this paper, we present RUBICON, an alternative architecture grounded in data management principles. Instead of delegating orchestration to an opaque agent, we introduce AQL (Agentic Query Language), a small, explicit query algebra - Find, From, and Where - executed through source-specific wrappers that enforce access control, schema alignment, and result normalization. All intermediate results are visible and inspectable. Complex questions are decomposed into structured, auditable query plans rather than hidden chains of LLM calls. Our thesis is simple: enterprise AI is not a prompt engineering problem; it is a systems problem. By reintroducing explicit query structure, wrapper-based mediation, and cost-based optimization, we obtain the breadth of agentic search while preserving traceability, determinism, and trust in enterprise environments.

翻译：过去几年中，“代理型人工智能”领域的主流叙事一直认为，大语言模型应通过动态选择工具、发出子查询和综合结果来协调信息访问。我们认为这种方法存在误导：企业面临的并非推理能力不足，而是数据集成问题。企业是数据驱动的：关键信息分散在异构系统中（例如数据库、文档和外部服务），每种系统都有自己的查询语言、模式、访问控制和性能约束。相比之下，当代基于LLM的架构优化用于对非结构化文本进行推理，并将企业系统视为语料库或由黑盒组件调用的外部工具。这导致了模式丰富、受管控且性能关键的数据系统与以文本为中心、概率性的LLM架构之间的不匹配，从而带来透明度有限、正确性保证薄弱以及性能不可预测等问题。在本文中，我们提出了RUBICON，一种基于数据管理原则的替代架构。我们不将协调工作委托给不透明的代理，而是引入AQL（代理查询语言），这是一种小型、显式的查询代数——Find、From和Where——通过特定源包装器执行，这些包装器强制执行访问控制、模式对齐和结果归一化。所有中间结果都是可见且可检查的。复杂问题被分解为结构化、可审计的查询计划，而非隐藏的LLM调用链。我们的论点很简单：企业AI不是一个提示工程问题，而是一个系统问题。通过重新引入显式查询结构、基于包装器的中介和基于成本的优化，我们在保留企业环境中可追溯性、确定性和信任的同时，获得了代理式搜索的广度。