Declarative by Design, Assistable Only by Convention: Benchmarking Multi-Agent Frameworks for AI-Assistability

Abstract

Multi-agent frameworks (MAFs) promise to simplify LLM-driven software development, yet no principled metric captures how well AI coding assistants can generate correct, framework-specific code. We introduce \textit{AI-assistability} ($\mathcal{A}\mathcal{I}$), a composite metric that quantifies a framework's amenability to AI-assisted development by combining structural alignment ($\bar{\ddot{I}}ƒ$) with functional correctness (pass@1). To evaluate this metric in a controlled setting, we design DDL2PropBank, a novel benchmark task that maps relational database schemas to PropBank semantic rolesets, and implement identical agent logic across ten frameworks using the Agent-as-a-Tool pattern. Our results challenge the intuition that declarative framework design guarantees AI-assistability: Agno, with a single canonical pattern and convention-aligned API, achieves the highest $\mathcal{A}\mathcal{I}$ score (0.55), while DSPy -- the most declarative framework by design -- scores lowest (0.07), as its novel abstractions are insufficiently represented in AI training data. We find that convention alignment, not declarative design alone, is the primary driver of AI-assistability ($r=0.576$ between $\bar{\ddot{I}}ƒ$ and pass@1). All artifacts -- DDL2PropBank, PropBank MCP server, and all implementations -- are available at https://github.com/ahmeshaf/ddl2propbank

ACM Submission

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