Day 77 of 100 Days of AI

Building and running AI agents is a messy process. I completed a draft crew of agents that can take a product name and provide summary of Youtube reviews. You can see a sample output below.

The first image is my terminal command, with the agents running.

Below is the final output, with links to Youtube videos in red.

I also run the agents (this time without source links) for a review of the BYD Atto 3 electric vehicle. The points below are helpful and are grounded from the agents doing research across a number of Youtube reviews.

The challenges I saw with this process though are:

• Costs — Running a few experiments cost me $3.28 for about half a dozen attempts at running the agents fully.
• Inconsistent outputs — Sometimes I get great outputs, and other times the agents fail completely or make up content.
• Slow and inefficient — The agents for this review app can take up to a minute to run fully, and occassionally take routes that are not necessary.

All these issues can be fixed with more powerful models, but by that point, would we have to build the agents ourselves? Or, would we just leave a more powerful LLM to figure things out rapidly and at a lower cost? That’s the view I wrote about here and increasingly, I’m starting to believe it.