LLM-based tools are automating more software development tasks at a rapid pace, but there is no rigorous way to evaluate how different architectural choices -- prompts, skills, tools, multi-agent setups -- materially affect both capability and cost. This paper introduces Scylla, an evaluation framework for benchmarking agentic coding tools through structured ablation studies that uses seven testing tiers (T0-T6) progressively adding complexity to isolate what directly influences results and how. The key metric is Cost-of-Pass (CoP): the expected dollar cost to get one correct solution, which directly quantifies the trade-off between complexity and efficiency. The framework is model-agnostic, designed to work with any CLI tool; this paper demonstrates it with Claude Sonnet 4.5, using multiple LLM judges (Opus 4.5, Sonnet 4.5, Haiku 4.5) from the same vendor for evaluation consensus, where judges score results using direct tests, human-designed LLM-evaluated rubrics, and qualitative assessment. The result is a reproducible framework that quantifies trade-offs between agent complexity and actual outcomes, suggesting that architectural complexity does not always improve quality.