Architecture as Capability Equalizer for Coding Agents

• URL: http://arxiv.org/abs/2608.21747v1
• Date: Sat, 22 Aug 2026 03:04:12 GMT
• Title: Architecture as Capability Equalizer for Coding Agents
• Abstract summary: LLM-based coding agents generate complete software systems from high-level descriptions.<n>We present a controlled experiment comparing five informationally equivalent specification formats.<n>Across 90 multi-turn agent trials, specification format shows a strong format x model interaction.
• Score: 0.8741110159894001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLM-based coding agents generate complete software systems from high-level descriptions, yet little is known about how the format of architecture specifications affects the quality of generated code or whether this effect depends on model capability. We present a controlled experiment comparing five informationally equivalent specification formats (informal prose, Mermaid diagrams with constraints and ADRs, OpenAPI, C4/Structurizr DSL, and TypeScript interface contracts with ArchUnit-style rules) across six models from three vendor families (Anthropic Claude, OpenAI GPT, Google Gemini). Across 90 multi-turn agent trials, specification format shows a strong format x model interaction. On the strongest models (Sonnet 4.6, GPT-5), format barely matters (quality spread 0.17-0.92). On weaker models, format produces spreads of 0.83-2.42 points, with code-proximate formats (OpenAPI, TypeScript contracts) recovering most of the capability gap. Mid-tier models can consume more tokens than frontier models for worse output when they enter compilation debugging loops that stronger models avoid. Self-validation rates collapse from 100% (Sonnet) to 0% (Gemini Flash) across the capability spectrum. TypeScript contracts triple API route coverage for the weakest model (33% to 100%). Structured architecture specifications serve as a capability equalizer, with value inversely proportional to model strength and the largest returns for cost-optimized deployments.

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