Aaron VanSledright

Tag: agentic ai

  • Using Strands to build an AWS Cost Analysis Agent

    Taking a break from Fantasy Football today to talk about a quick weekend project I put together.

    A friend of mine was chatting about how their AWS costs are getting out of control and they aren’t sure where to start when it comes to cleaning up the account. This prompted me with an idea to utilize AI to build an Agent that can interact with your AWS account to review resources, provide cost analysis and give you clear CLI commands or console instructions to help clean up the account.

    In order to do this, I wanted to incur as little cost as possible. So, I built a Docker image in order to run it locally. First, there is a shell script that will build an IAM User in your account that provides read-only access to the account, Cost Explorer access and access to Bedrock (to communicate with an AI model).

    The Docker image runs and builds an Agent that interacts with whichever model you want to utilize. I picked Amazon’s Nova model just to keep the costs down. The container then presents a web interface where the account’s bill break down will be displayed:

    It will also display some common costly resources and their counts:

    The next block is where things get very helpful. The AI will present to you suggestions as to how to save some money as well as some risk calculations. Because I ran this against my real account I had to blur out some information but you get the idea:

    So, now you have some actionable activities to work through to help you save money on your AWS bill. But what if you have more questions? I also included a simple chat box to help you work with the bot to come up with other explanations or find other ways to save cost.

    So I asked the AI to find the largest instance in my account and then determine the right size for it. Here is the response:

    Why would this be important? Well, if you had the AI review all of the instances in your account you could identify EC2 instances that are oversized and have them be changed accordingly. After I implemented a few of the changes that the AI recommended (and verified they didn’t break anything), my account billing decreased by about $100.

    If this is something you are interested in running on your own account feel free to reach out! I’d be happy to help you setup up the container on your machine and make suggestions as to how to save some money!

  • AI Loses Its First Matchup – Fantasy Football Agentic AI

    Straight to the point. AI lost is week one matchup by 2.28 points. I watched as many of the games as I could so that I could give a slight bit of commentary.

    First a re-cap. If you haven’t been following along, I have built and am continuing to improve upon an Agentic AI solution for drafting and managing a Fantasy Football team for the 2025 season. The team is entirely AI selected and you can see its predictions for week 1 here.

    There was a couple of concerns that I had looking at the lineup. Most notably Sam Darnold in the superflex (OP) position as I thought some of the other players might have break out games and boy was I right!

    Here is the results from week 1

    Now, let’s comment on a few things. George Kittle left his game with an injury and is likely to miss a few weeks. AI can’t predict in game injuries, yet. DJ Moore was the final hope Monday night and he was either not targeted when he was open or Caleb Williams simply didn’t throw a good ball. AI, can’t predict in game performance, yet.

    Now, the Agent did hit on Josh Allen with his amazing performance against the Ravens. Breece Hall was also a great pick beating his projections.

    What’s Next?

    So we have some clear things to work out.

    1. Injuries – the AI Coach needs to understand that Kittle is likely out for a few weeks.
    2. Waivers – Now that we have an injury we need to replace a player. Engram is on the bench but is he the best tight end?

    With these clear needs in mind I am actively working on building out a waiver wire monitoring tool to grab available players from the ESPN Fantasy platform. Because ESPN doesn’t have a native API this has been particularly challenging. I added in a Lambda function that will run daily and update the other teams rosters in a DynamoDB table so that we could potentially compare lists of players from other sources. This would give us a subset of “available” players. I also will be adding in an injury parameter that will help assist the Agent in determining the next lineup. Finally, I am scraping out the fantasy points earned per team and storing them as another data set that the Agent can use to help make predictions.

    Current architecture diagram:

    I’m also looking heavily into how I can structure all the data more efficiently so there is less infrastructure to manage. Ideally, it would be nice to have a single table with the player as the primary key and all of the subsets of data underneath.

    I think the AI is close to dominating the rest of the league! I will be posting its predictions for next week sometime on Thursday before the game!