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

TL;DR

This paper extends a goals-to-aspects methodology to agentic AI systems, providing a pattern language for systematically modularizing crosscutting concerns like security and reliability using AOP in Rust.

Contribution

It introduces a pattern language with 12 reusable patterns for agentic AI, including new patterns for agent-specific concerns, and extends the V-graph model for goal-softgoal contribution analysis.

Findings

Validated pattern language through a case study on an autonomous agent framework.

Demonstrated systematic identification and modularization of crosscutting concerns.

Extended the V-graph model for better goal and soft-goal contribution analysis.

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.