mlsanityData to 7 Oct 2026

Architecture Without Architects: How AI Coding Agents Shape Software Architecture

arXiv 2604.04990 ↗First posted 5 Apr 2026cs.SE cs.AIcited by 2

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

AI coding agents select frameworks, scaffold infrastructure, and wire integrations, often in seconds. These are architectural decisions, yet almost no one reviews them as such. We identify five mechanisms by which agents make implicit architectural choices and propose six prompt-architecture coupling patterns that map natural-language prompt features to the infrastructure they require. The patterns range from contingent couplings (structured output validation) that may weaken as models improve to fundamental ones (tool-call orchestration) that persist regardless of model capability. An illustrative demonstration confirms that prompt wording alone produces structurally different systems for the same task. We term the phenomenon vibe architecting, architecture shaped by prompts rather than deliberate design, and outline review practices, decision records, and tooling to bring these hidden decisions under governance.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 31 references that are in the map. First posted on arXiv 5 Apr 2026.

Builds on

14 of 31 references.

Closest ancestors · 2

Same direction, newest first

  1. 2026Agentic Much? Adoption of Coding Agents on GitHub26 Jan
  2. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation16 Aug

Other references · 12

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  5. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
  6. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  7. 2024Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Servecited by 507
  8. 2025Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Reviewcited by 28
  9. 2025Vibe coding: programming through conversation with artificial intelligencecited by 26
  10. 2025Software Architecture Meets LLMs: A Systematic Literature Reviewcited by 10
  11. 2025Promptware Engineering: Software Engineering for Prompt-Enabled Systemscited by 6
  12. 2026"TODO: Fix the Mess Gemini Created": Towards Understanding GenAI-Induced Self-Admitted Technical Debtcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
  2. 2026The Devil Is in the Interface: Evaluating How Tool Architecture Shapes Coding Agent Behavior
  3. 2026Theory of Code Space: Do Code Agents Understand Software Architecture?
  4. 2025Decoding the Configuration of AI Coding Agents: Insights from Claude Code Projects
  5. 2026Architectural Design Decisions in AI Agent Harnesses
  6. 2026AI-Generated Smells: An Analysis of Code and Architecture in LLM and Agent-Driven Development
  7. 2025Vibe Coding: Toward an AI-Native Paradigm for Semantic and Intent-Driven Programming ↗
  8. 2026The Empire, Long Divided, Must Unite: Architectural Convergence in Three LLM Agent Harnesses