# Notebook Overview

## Motivation

Tabular data reasoning continues to be an exciting application of Agents, particularly in the financial domain, where multiple reasoning steps may be needed to identify the right answer. For example, a financial analyst working with financial statements may be interested in computing various financial ratios with natural language queries.

Some examples may include:

* ROE (Return on Equity) = Net Income / Shareholder's Equity
* Net Profit Margin = Net Income / Revenue
* Asset Turnover = Revenue / Average Total Assets
* Financial Leverage = Average Total Assets / Shareholder's Equity

Having an Agent which is able to correctly compute these and other ratios would be a great help for any analyst in the field of Finance.

## Objective

In this notebook we explore how to setup a [Cohere Agent](https://docs.cohere.com/docs/multi-step-tool-use) to answer questions over tables in Apple's SEC10K 2020 form. [Financial CSV Agent](https://docs.cohere.com/page/csv-agent) already showed how to use Langchain to ask questions about your data. This notebook will demonstrate how you can build the same agent using Cohere's native API with Langchain Python tool. We will also explore how to make your agent more resilient to errors.