Arbel, Goldstein, and Salib's Algorithmic Corporation (A-corp) proposal offers the most architecturally sophisticated legal response yet to the problem of AI agent individuation. Its diagnosis is sound: existing entity law cannot identify AI wrongdoers, and governing harmful AI behavior requires instruments that reach beyond human principals. The cure, however, rests on an unexamined structural assumption. This article identifies that assumption and argues that it fails. The A-corp's thick-identity solution presupposes that AI agents can be treated as persistent entities at stable, classifiable intentionality levels, what I call the Static Agent Assumption. Drawing on Asymmetric Intentionality Theory, the Generalized Intentionality Mismatch Theorem (GIMT), and multilevel Evolutionary Game Theory, I show that the assumption fails on three independent grounds. Post-RLVR agents transit between intentionality levels within a single task; legal incentives calibrated to a stable level misfire precisely during the execution phases generating the highest-volume harmful actions. I formalize this as Dynamic Classification Failure, the sixth mode of the GIMT. The emergent-selection mechanism operates at evolutionary timescales structurally mismatched to harm-accumulation rates in capable AI systems, functioning retrospectively as harm-pricing rather than prospectively as prevention. Mandatory institutionalization generates hysteretic lock-in; applying the Constitutional Lock-in Index, projected scores reach 0.75, placing the A-corp mandate among historically reform-resistant governance structures. I propose The Responsibility Ramp as a dynamic alternative: task-phase-level intentionality classification, graduated liability scaled to operative cognitive architecture at the moment of harm, and attribution tracing to the configuration decision that specified the harmful objective. The Ramp is compatible with the A-corp's cryptographic registry, which should be legislated independently.