[2603.00472] From Goals to Aspects, Revisited: An NFR Pattern Language for Agentic AI Systems

Computer Science > Artificial Intelligence arXiv:2603.00472 (cs) [Submitted on 28 Feb 2026] Title:From Goals to Aspects, Revisited: An NFR Pattern Language for Agentic AI Systems Authors:Yijun Yu View a PDF of the paper titled From Goals to Aspects, Revisited: An NFR Pattern Language for Agentic AI Systems, by Yijun Yu View PDF Abstract:Agentic AI systems exhibit numerous crosscutting concerns -- security, observability, cost management, fault tolerance -- that are poorly modularized in current implementations, contributing to the high failure rate of AI projects in reaching production. The goals-to-aspects methodology proposed at RE 2004 demonstrated that aspects can be systematically discovered from i* goal models by identifying non-functional soft-goals that crosscut functional goals. This paper revisits and extends that methodology to the agentic AI domain. We present a pattern language of 12 reusable patterns organized across four NFR categories (security, reliability, observability, cost management), each mapping an i* goal model to a concrete aspect implementation using an AOP framework for Rust. Four patterns address agent-specific crosscutting concerns absent from traditional AOP literature: tool-scope sandboxing, prompt injection detection, token budget management, and action audit trails. We extend the V-graph model to capture how agent tasks simultaneously contribute to functional goals and non-functional soft-goals. We validate the pattern language through a c...