Cascade Alignment Under Semantic Compression: A Pilot Study of System-Prompt Identity Layers in Agent Architectures

We examine how compressing the layered system-prompt cascade of an LLM-backed agent affects identity-level alignment, measured by keyword recurrence in the agent’s responses to identity-probing prompts. Using a 15-call pilot (3 conditions × 5 prompts × 1 sample) against claude-haiku-4-5, we replaced the cascade’s vision and department layers with content-hash + 120-character semantic summaries. Aggressive compression (5.71×, 337 → 59 tokens) cut absolute alignment by 54% (24/75 → 11/75 keyword hits), but per-token information density rose 2.62×. A hybrid condition preserving the vision layer while compressing the department layer (1.55× compression) preserved 87.5% of alignment, suggesting an asymmetry: the cross-domain bridging layer resists compression while the role-specific layer absorbs it. We further observe that compression failures concentrate on cross-domain prompts (where the cascade must bridge two of its layers) rather than on-axis prompts. The findings are limited by small sample, single agent, single model, and a blunt keyword-counting alignment proxy. We position this study as a pilot within a broader evaluation framework (SHI Eval Framework) and outline the larger study (5 agents × 25 prompts × 5 samples with an LLM-based personality evaluator) that this work is a precursor to. AI disclosure. This work was produced with an agentic AI research apparatus operated by Saluca Labs. The apparatus drafted, searched and analysed under direction. Cristian Ruvalcaba is the human author and is accountable for the content. No AI system is listed as an author or contributor, because authorship entails accountability that a model cannot hold; this disclosure is the credit, and it is deliberately the whole of it.