The interesting part here isn’t just the <$500 figure; it’s that the 21-day run surfaced the visual checks needed before production. That makes evaluation look more like a pipeline question: how much context can the agent retain between migration steps, and which observations are worth carrying forward? In my own experiments I’ve been looking at Contextpress (GitHub - Taha-azizi/contextpress: Deterministic context compression for LLM chat, RAG, and agent pipelines · GitHub · contextpress · PyPI), a deterministic local compressor for message lists, as a way to drop duplicate tool output while preserving decisions and constraints. I’d want to compare migrations by replaying with a fixed budget and tracking review findings, retries, and regressions—not token spend alone. The visual checks seem especially valuable because they expose failures that a clean typecheck can miss.