How would you build an automated commentary engine for daily trade attribution at scale? [R]

Hey everyone,

I'm currently working through a problem in the market risk reporting space and would love to hear how you all would architect this.

The Use Case: > I have thousands of trades coming in at varying frequencies (daily, monthly). I need to build a system that automatically analyzes this time-series data and generates a precise, human-readable commentary detailing exactly what changed and why.

For example, the output needs to be a judgment like: "The portfolio variance today was +$50k, driven primarily by a shift in the Equities asset class, with the largest single contributor being Trade XYZ."

The Dilemma:

• The Math: Absolute precision is non-negotiable. I know I can't just dump raw data into an LLM and ask it to calculate attribution, because it will hallucinate the math. I usually rely on Python and Polars for the high-performance deterministic crunching.
• The Rigidity: If I hardcode every single attribution scenario (by asset class, by region, by specific trade) into a static ETL pipeline before feeding it to an LLM for summarization, the system becomes too rigid to handle new business scenarios automatically.

My Question:

How would you strike the balance between deterministic mathematical precision and dynamic natural language generation?

Are you using Agentic workflows (e.g., having an LLM dynamically write and execute Polars/pandas code in a sandbox)? Or are you sticking to pre-calculated cubes and heavily structured context prompts? Any specific frameworks (LangChain, LlamaIndex, PandasAI, etc.) or design patterns you've had success within financial reporting?

Appreciate any insights!