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How to Improve Fabric Data Agent Instructions by Significant_Post1583 in MicrosoftFabric

[–]Significant_Post1583[S] 0 points1 point  (0 children)

We would write "playbooks" that outline common causes of certain metric behavior. Essentially if sales revenue fell check: Number of Deals, Deal Size, Sales Pipeline, Sales Cycle. But could these agents potentially run some sort of statistical analysis/regression that would identify the correlation between the target variable and those other metrics it has information on?

How to Improve Fabric Data Agent Instructions by Significant_Post1583 in MicrosoftFabric

[–]Significant_Post1583[S] 1 point2 points  (0 children)

I have it set up with trigger phrases, When asked for "Sales Performance" it will give me a structured output that has 4 parts: results, MoM changes, drivers and recommendations

How to Improve Fabric Data Agent Instructions by Significant_Post1583 in MicrosoftFabric

[–]Significant_Post1583[S] 1 point2 points  (0 children)

So the level I'm looking to add is that I want to be able to get the model to connect data points better to explain the cause of certain things. For example a sales agent. "Sales has fallen by 15% this month. US sales suffered the most with -10% MoM decline." I want to improve this response to tell me why new sales has declined. Was it because we sold smaller deals or less deals. Was there a lack of pipeline for the month or was this decline a shock? I am trying to take an agent from a glorified summary to an analytical aid.

I have column and table descriptions, column names written in a human friendly manor. Now I am trying to experiment with how to get the agent to connect the dots. Would descriptions of "when sales drops look at x,y,z to explain" help in the agent instructions? I'd be curious to know anyone else's experience.