AI Agents: Building Teams of LLM Agents that Work For You
About Course
In this course you’ll learn about this new way of using LLM Agents: deploying multiple agents to work together as teams to accomplish more complex tasks for you!
Everything is taught step by step and the course is fully practical with multiple examples and one complete AI Agents-based App that we build together.
One of the things we use to accomplish this is ChatGPT’s API so we can use ChatGPT through Python.
We also use AutoGen to enable our Agents to work together and communicate with one another (to accomplish tasks with no human intervention).
We also provide a few optional sections. One of these sections teaches to have a front-end, using Streamlit, to more easily interact with your AI Agents.
Another optional section is for those who want to run AI Agents at scale! Here we show you how to deploy your LLM Agents on Google Cloud, so anyone can use your product.
Lastly, one more optional section is available showing how to set up a payment system/subscription model using Stripe for those who want to monetize their AI Agents-based App!
Everything is explained simply and in a step-by-step approach. All code shown in the course is also provided.
Please not that the OpenAI API is not free, you will need to fund your OpenAI developer account with about $5-10 to follow through with the class and build your own app. We clearly show and explain how to do this and minimize your OpenAI costs during this class.
Course Content
1 – Intro to LLM Agents
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001 -Defining LLMs.mp4
00:00 -
002 -Building an Autocomplete.mp4
00:00 -
003 -From Autocomplete to LLMs.mp4
00:00 -
004 -Prompt Engineering.mp4
00:00 -
005 -Agentic Design.mp4
00:00
2 – LLM Agents Implementation
3 – AutoGen Chat Structures
4 – Application Using Agents for Stock Analysis
5 – Deployment Your AI Agent App
6 – Add Subscription – Payments to your App
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