Understanding an AWS AI agent before building one

An AI agent becomes easier to understand when its parts are separated. A language model produces an answer, while a tool lets the system retrieve information or perform a specific action. An agent loop connects the two: decide what information is needed, call a tool, inspect the result, and decide what should happen next.

AWS's introductory Cost Explainer example makes this concrete. A question about a changing bill is connected to a cost breakdown tool. The model then explains the numbers returned by that tool. The explanation should stay grounded in those numbers rather than inventing causes or amounts.

Before implementing an example, define the exact question, the information the tool must return, and how the answer will be checked. These three decisions make it easier to keep the scope small.

Start with a predictable dataset. Mock data makes it possible to test a cost increase, an unchanged bill, and missing information without exposing billing details. Compare the output with the input values. Check that the totals agree, percentage changes are consistent, and an empty result does not produce an invented explanation.

After that, review the real integration's permissions, operating costs, and what information could appear in logs. A useful first exercise is to change the mock values and check whether the explanation changes consistently before connecting any live account.

This is a reading note, not a report of a deployed project.

Source: Build Your First AI Agent on AWS , by Vishal Alhat.