Architecture Without Architects: How AI Coding Agents Shape Software Architecture

Phongsakon Mark Konrad; Tim Lukas Adam; Riccardo Terrenzi; Serkan Ayvaz

cs.SEApr 5, 2026

Architecture Without Architects: How AI Coding Agents Shape Software Architecture

Authors: Phongsakon Mark Konrad, Tim Lukas Adam, Riccardo Terrenzi, Serkan Ayvaz

Organizations: Centre for Industrial Software, University of Southern Denmark, Alsion 2, Sønderborg, 6400, Denmark

Abstract

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.

Figures & tables

Variant	Frmwk	Store	Components	Integr.	LoC	Files
A: FAQ	Express	JSON	FAQ loader, chat handler	API call	141	2
B: JSON	Express	—	Zod schema, retry, fallback	JSON mode	472	4
C: Tools	Express	SQLite	Tool registry, agent loop, store	Func. call	827	6

TABLE I: Architectural Divergence Across Three Prompt Variants

Panel of Figure 2 — Fig. 1: Architectural components introduced by each prompt variant. Darker shading indicates components added by increasing prompt specificity. Variant A passes FAQ context to the LLM and returns free-form text; Variant B adds Zod validation, JSON Schema mode, and retry logic; Variant C adds a tool registry, agent loop, and SQLite state management.

Pattern	Trigger	Infrastructure Required	Quality Impact	Type
Constraint patterns
1A: Structured Output [ 8 , 30 ]	JSON schema	Parser, validator, retry, fallback	Interoperability, robustness	Contingent
1B: Few-Shot [ 9 ]	Dynamic examples	Embedding, vector store, curator	Capability, grounding	Contingent
Capability patterns
2A: Function Calling [ 30 , 31 ]	Tool signatures	Router, validator, error handler, agent loop	Extensibility, attack surface	Fundamental
2B: ReAct Reasoning [ 8 , 32 ]	CoT with tools	State machine, validator, timeout handler	Autonomy, testability	Fundamental

TABLE II: Prompt-Architecture Coupling Patterns

Panel of Figure 4 — Fig. 2: Composition of coupling patterns in a single system (RAG chatbot with tool access). Pattern IDs match the catalog in Section IV . Cross-cutting concerns (dashed) span multiple patterns and add infrastructure no single pattern accounts for on its own.

Panel of Figure 5 — Fig. 3: Three-layer framework for architecture-aware AI-assisted development. Each layer maps tool mechanisms (left) to SA concepts (right).

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Formal Architecture Descriptors as Navigation Primitives for AI Coding Agents

Ruoqi Jin

Independent Researcher, China

AI Coding Agents Software Engineering

Open paper AskPDF arXiv

AI coding agents spend a substantial fraction of their tool calls on undirected codebase exploration. We investigate whether providing agents with formal architecture descriptors can reduce this navigational overhead. We present three complementary studies. First, a controlled experiment (24 code localization tasks x 4 conditions, Claude Sonnet 4.6, temperature=0) demonstrates that architecture context reduces navigation steps by 33-44% (Wilcoxon p=0.009, Cohen's d=0.92), with no significant format difference detected across S-expression, JSON, YAML, and Markdown. Second, an artifact-vs-process experiment (15 tasks x 3 conditions) demonstrates that an automatically generated descriptor achieves 100% accuracy versus 80% blind (p=0.002, d=1.04), proving direct navigational value independent of developer self-clarification. Third, an observational field study across 7,012 Claude Code sessions shows 52% reduction in agent behavioral variance. A writer-side experiment (96 generation runs, 96 error injections) reveals critical failure mode differences: JSON fails atomically, YAML silently corrupts 50% of errors, S-expressions detect all structural completeness errors. We propose intent.lisp, an S-expression architecture descriptor, and open-source the Forge toolkit.

Abstract

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