Cool stuff. I'm the CTO of Stargazr (stargazr.ai), a financial & operational AI for manufacturing companies; we started using transformers to process financial data in 2020, a bit before the GPT boom.
In our experience, things beyond very constrained function calling opens the door to explainability problems. We moved away from "based on the embeddings of this P&L, you should do X" towards "I called a function to generate your P&L, which is in this table; based on this you could think of applying these actions".
It's a loss in terms of semantics (the embeddings could pack more granular P&L observations over time) but much better in terms of explainability. I see other finance AIs such as SAP Joule also going in the same direction.
In our experience, things beyond very constrained function calling opens the door to explainability problems. We moved away from "based on the embeddings of this P&L, you should do X" towards "I called a function to generate your P&L, which is in this table; based on this you could think of applying these actions".
It's a loss in terms of semantics (the embeddings could pack more granular P&L observations over time) but much better in terms of explainability. I see other finance AIs such as SAP Joule also going in the same direction.