Critiques

This section analyzes structural failures in prevailing AI paradigms, tooling, and execution models. The purpose is not to express opinion, but to identify architectural defects that cannot be resolved by incremental improvements.

Critiques in this section examine:

Model-centric control mechanisms (e.g., RAG, MCP)
Context-window and embedding-based governance assumptions
Token-driven execution models that lack semantic constraints
Failure points that prevent verifiable delegation and completion

Each critique clarifies why certain approaches are incompatible with semantic execution, and why they cannot serve as the foundation for scalable, accountable AI systems.

An Architectural Critique of RAG and MCP

Retrieval-Augmented Generation (RAG) and Model Context Protocols (MCP) are often presented as architectural advances for large language models. This paper argues that they are not. RAG and MCP are compensatory techniques that emerge from model-centric system design. They attempt to patch the absence of persistent semantic architecture by overloading the context window with responsibilities it was never designed to bear. From a systems perspective, this represents regression engineering rather than architectural progress. ...