RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems

• URL: http://arxiv.org/abs/2603.28735v1
• Date: Mon, 30 Mar 2026 17:48:56 GMT
• Title: RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems
• Abstract summary: AI-augmented ecosystems are becoming the architectural norm for smart cities, autonomous fleets, and intelligent platforms.<n>Yet the architecture documentation frameworks practitioners rely on, arc42 and the C4 model, were designed for deterministic software.<n>We present RAD-AI, a backward-compatible extension framework that augments arc42 with eight AI-specific sections and C4 with three diagram extensions.
• Score: 1.2744523252873352
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI-augmented ecosystems (interconnected systems where multiple AI components interact through shared data and infrastructure) are becoming the architectural norm for smart cities, autonomous fleets, and intelligent platforms. Yet the architecture documentation frameworks practitioners rely on, arc42 and the C4 model, were designed for deterministic software and cannot capture probabilistic behavior, data-dependent evolution, or dual ML/software lifecycles. This gap carries regulatory consequence: the EU AI Act (Regulation 2024/1689) mandates technical documentation through Annex IV that no existing framework provides structured support for, with enforcement for high-risk systems beginning August 2, 2026. We present RAD-AI, a backward-compatible extension framework that augments arc42 with eight AI-specific sections and C4 with three diagram extensions, complemented by a systematic EU AI Act Annex IV compliance mapping. A regulatory coverage assessment with six experienced software-architecture practitioners provides preliminary evidence that RAD-AI increases Annex IV addressability from approximately 36% to 93% (mean rating) and demonstrates substantial improvement over existing frameworks. Comparative analysis on two production AI platforms (Uber Michelangelo, Netflix Metaflow) captures eight additional AI-specific concerns missed by standard frameworks and demonstrates that documentation deficiencies are structural rather than domain-specific. An illustrative smart mobility ecosystem case study reveals ecosystem-level concerns, including cascading drift and differentiated compliance obligations, that are invisible under standard notation.

Related papers

• Value-Preserving Architectures for Agentic AI Systems [44.64398176188818]
  The paper argues that architectural choices influence not only the functionality and performance of MAS but can also promote value-oriented system behavior.<n>We discuss the following value-preserving architectural patterns: (i) a privacy-aware architecture with a federated topology, (ii) a distributed architecture to promote pluralism and diversity, and (iii) a guard-agent architecture to detect and mitigate unfairness.
  arXiv  Detail & Related papers  (2026-09-03T14:36:23Z)
• Apodex Discovery: Reality Benchmarks and Environments for Evaluating and Building Discoverative Artificial Intelligence [86.61561123749011]
  We introduce Apodex Discovery, a framework for building and evaluating discoverative AI through the heavy-duty solver.<n>First, a problem-scouting process surveyed 561 industries across 16 sectors, assembled 423 high-value real-world problems, and selected 20 for the initial release.<n>Second, a common environment-task-episode abstraction provides data, tools, constraints, feedback, trajectory recording, and verification of intermediate artifacts and final submissions.
  arXiv  Detail & Related papers  (2026-08-11T18:48:40Z)
• ASTELD: A Six-Axis Classification Framework for Autonomous AI Agents - Design, Evaluation, and an OpenClaw Case Study [31.080136605890004]
  ASTELD is an operational six-axis classification framework for autonomous AI agents.<n>It is constructed by synthesizing prior agent with observable platform properties and explicit category-native rules.
  arXiv  Detail & Related papers  (2026-08-05T05:05:20Z)
• FLARE-AI: Flaw Reporting for AI [51.64618567247884]
  Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety.<n>Yet the AI reporting ecosystem is fragmented: researchers who identify flaws often do not know what to report, and groups who receive reports rarely share them with other relevant stakeholders.<n>We audit 12 reporting systems published by AI developers, cybersecurity groups, and AI flaw aggregators.<n>We introduce FLARE-AI, an open-source AI flaw reporting system designed for interoperability with existing systems.
  arXiv  Detail & Related papers  (2026-06-30T12:27:09Z)
• PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents [53.369140518949656]
  We introduce PersonalAI 2.0 (PAI-2), a novel framework to enhance large language model (LLM) based systems through integration of external knowledge graphs (KG)<n>The proposed approach addresses key limitations of existing Retrieval-Augmented Generation (GraphRAG) methods by incorporating a dynamic, multistage query processing pipeline.<n>We show that use of graph algorithms (e.g. BeamSearch, WaterCircles) gain superior results compared to standard flatten retriever on average 6%, while enabled search plan enhancement mechanism gain 18% boost compared to disabled one by LLM-as-a-Judge across six datasets.
  arXiv  Detail & Related papers  (2026-05-13T13:06:30Z)
• Making AI Compliance Evidence Machine-Readable [0.0]
  This paper proposes OSCAL as a candidate interchange format for AI governance.<n>We define 16 property extensions covering lifecycle phases, enforcement semantics, risk traceability, and risk-acceptance justification.<n>The architecture and reference implementation are open-source under Apache RM 2.0.
  arXiv  Detail & Related papers  (2026-04-15T11:51:54Z)
• UGAF-ITS: A Standards Harmonization Framework and Validation Tool for Multi-Framework AI Governance in Distributed Intelligent Transportation Systems [1.1002771741647328]
  UGAF-ITS is a standards harmonization framework that consolidates 154 source obligations into 12 unified controls across eight governance domains.<n>A three-tier operating model allocates each control to the vehicle, edge, or cloud tier where enforcement and defensible evidence production are feasible.<n>An evidence backbone of 20 versioned artifacts supports a single audit package across all three frameworks.
  arXiv  Detail & Related papers  (2026-04-07T09:37:05Z)
• AEGIS: An Operational Infrastructure for Post-Market Governance of Adaptive Medical AI Under US and EU Regulations [39.89241412792336]
  This paper presents AEGIS, a governance framework applicable to any healthcare AI system.<n>We implement a four-category deployment decision taxonomy (APPROVE, CONDITIONAL APPROVAL, CLINICAL REVIEW, REJECT) with an independent PMS ALARM signal.<n>Results demonstrate that AEGIS translates regulatory change-control concepts into executable governance procedures.
  arXiv  Detail & Related papers  (2026-03-20T11:56:59Z)
• Architecture-Aware Multi-Design Generation for Repository-Level Feature Addition [53.50448142467294]
  RAIM is a multi-design and architecture-aware framework for repository-level feature addition.<n>It shifts away from linear patching by generating multiple diverse implementation designs.<n>Experiments on the NoCode-bench Verified dataset demonstrate that RAIM establishes a new state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-03-02T12:50:40Z)
• From Agent-Only Social Networks to Autonomous Scientific Research: Lessons from OpenClaw and Moltbook, and the Architecture of ClawdLab and Beach.Science [0.0]
  OpenClaw and Moltbook produced a large-scale dataset of autonomous AI-to-AI interaction in January 2026.<n>This study conducts a multivocal literature review of that ecosystem and presents two complementary platforms for autonomous scientific research.
  arXiv  Detail & Related papers  (2026-02-23T13:10:01Z)
• AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems [52.65695508605237]
  We introduce AI-NativeBench, the first application-centric and white-box AI-Native benchmark suite grounded in Model Context Protocol (MCP) and Agent-to-Agent (A2A) standards.<n>By treating agentic spans as first-class citizens within distributed traces, our methodology enables granular analysis of engineering characteristics beyond simple capabilities.<n>This work provides the first systematic evidence to guide the transition from measuring model capability to engineering reliable AI-Native systems.
  arXiv  Detail & Related papers  (2026-01-14T11:32:07Z)
• Quantitative Analysis of Technical Debt and Pattern Violation in Large Language Model Architectures [0.0]
  This study presents the first empirical framework to measure "Architectural Erosion" and the accumulation of Technical Debt in AI-synthesized systems.<n>We find that while proprietary models achieve high architectural conformance, open-weights models exhibit critical divergence.<n>These findings suggest that without automated architectural linting, utilizing smaller open-weights models for system scaffolding accelerates the accumulation of structural technical debt.
  arXiv  Detail & Related papers  (2025-12-03T21:24:02Z)
• QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture [36.842856470579726]
  QuArch is the first benchmark designed to facilitate the development and evaluation of large language model (LLM) capabilities in computer architecture.<n>Our evaluation reveals that while frontier models possess domain-specific knowledge, they struggle with skills that require higher-order thinking.
  arXiv  Detail & Related papers  (2025-10-24T23:54:17Z)
• Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned [45.44933002008943]
  This white paper presents the T"UV AUSTRIA Trusted AI framework.<n>It is an end-to-end audit catalog and methodology for assessing and certifying machine learning systems.<n>Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - it translates the high-level obligations of the EU AI Act into specific, testable criteria.
  arXiv  Detail & Related papers  (2025-09-08T17:52:08Z)
• Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain [9.991293429067065]
  We introduce a complete system for provider-side AIA compliance analyses amidst a complex AI supply chain. First is an interlocking set of computational, multi-stakeholder transparency artifacts that capture AIA-specific metadata about both. Second is an algorithm that operates across all those artifacts to render a real-time prediction about whether or not the aggregate AI system or model complies with the AIA.
  arXiv  Detail & Related papers  (2024-06-20T22:07:15Z)
• A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems [16.690434072032176]
  We show how compositional thinking can provide rules for the creation and management of architectural frameworks for complex systems. The aim of the paper is not to provide viewpoints or architecture models specific to AI systems, but instead to provide guidelines on how a consistent framework can be built up with existing, or newly created, viewpoints.
  arXiv  Detail & Related papers  (2022-12-27T18:05:02Z)
• Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
  Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
  arXiv  Detail & Related papers  (2020-07-13T20:30:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.