The "cool visuals over verified results" critique is valid but undersells the harder problem underneath. For any Wall Street deployment you need more than results-match-reality, you need a decision record that satisfies MiFID II / FINRA rules about investment recommendation audit. Persistent workspaces + memory files are a feature for the researcher and a regulatory question mark for compliance. If a regulator asks "what did the agent recommend last Tuesday and which data snapshot did it reason over?", can you answer? With the auto-generated Python module approach you'd need to pin module version, data fetch timestamps, model version, and prompt state, all to a single immutable record per recommendation. None of that falls out of a normal agent framework. Curious whether LangAlpha has thought about signed execution logs per session. Not as a sales feature but as a prerequisite for a firm running this in anger. Financial is one of the few verticals where "we don't know what we said yesterday" is a deployment blocker.