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

• URL: http://arxiv.org/abs/2603.00472v1
• Date: Sat, 28 Feb 2026 05:18:38 GMT
• Title: From Goals to Aspects, Revisited: An NFR Pattern Language for Agentic AI Systems
• Abstract summary: Agentic AI systems exhibit numerous crosscutting concerns that are poorly modularized in current implementations.<n>We present a pattern language of 12 reusable patterns organized across four NFR categories (security, reliability, observability, cost management)<n>Four patterns address agent-specific crosscutting concerns absent from traditional AOP literature.
• Score: 3.2609949130799247
• License: http://creativecommons.org/licenses/by/4.0/
• 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 case study analyzing an open-source autonomous agent framework, demonstrating how goal-driven aspect discovery systematically identifies and modularizes crosscutting concerns. The pattern language offers a principled approach for engineering reliable agentic AI systems through early identification of crosscutting concerns.

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