Question

A financial analytics team is building a Bedrock Agent that lets analysts ask natural-language questions about uploaded CSV files, such as 'calculate the 90-day moving average of these transactions and plot the trend.' The agent must dynamically write and execute Python to perform statistical computations and generate charts on the uploaded data, without the team pre-building an action group for every possible calculation. Which approach best satisfies this requirement?

Accepted Answer

Enable the code interpreter capability on the Bedrock Agent so it can generate and run Python code in a sandboxed environment against user-uploaded files.

Answer

Create an action group backed by a Lambda function that hard-codes moving-average and charting logic, and expand it with new functions as analysts request them.

Answer

Attach a Bedrock Knowledge Base containing the CSV files so the agent can retrieve and summarize numeric values through semantic search.

Answer

Use the Converse API with tool use to return the raw CSV rows to the model and let the foundation model perform the arithmetic directly in its response.