Scaling up
So far, you have focused on getting an agent to help you build something that works. The next step is turning that prototype into a solution that is easier to maintain, reuse, share, and put into the hands of other people.
From prototype to real solution
A project that works once on your laptop is a good start. But real business solutions usually need more. You need to keep track of changes, preserve workflows you will use again, connect AI directly to the product when it adds value, and make the finished solution accessible to other people.
Build something that works
↓
Keep track of changes
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Extend the agent with reusable capabilities
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Put AI inside the solution when useful
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Share and deploy itThis module introduces those next steps. You do not need to become a professional software engineer. The goal is to understand the basic ideas well enough to build solutions that are safer to change and easier for other people to use.
What you'll be able to do
By the end of this module, you should be able to use Git and GitHub to keep a history of your work, understand how reusable workflows can become more explicit agent capabilities such as skills and hooks, understand how an app can call an AI model through an API, and deploy a simple solution so someone else can actually use it.
Planned sub-pages
1. Keep track of changes
Learn the basic idea behind version control and use Git and GitHub to
save checkpoints, inspect what changed, recover an earlier version, and
collaborate without passing around files named
final_v7_really-final.xlsx.
2. Extend the agent: skills and hooks
You already know how to save and reuse a workflow. Here, we take the next step: turning common workflows into more explicit agent capabilities. Learn how skills package instructions and resources the agent can invoke when needed, and how hooks automatically trigger actions at specific points in the workflow.
3. Put AI inside your solution
So far, you have mostly used AI to build things. Now learn how AI can become part of the thing you build—for example, a website that sends a user's input to an LLM and uses the response in the workflow. We will introduce APIs, model calls, structured outputs, API keys, cost, and basic safeguards.
4. Share and deploy what you build
Move from “it works on my computer” to something another person can actually use. Learn the basic ideas behind hosting, deployment, environment variables, secrets, updates, and maintaining a solution after it goes live.
What about subagents and agent teams?
Once the core ideas above are in place, you can also experiment with subagents that take on specialized parts of a larger task, or agent teams that coordinate several agents at once. These are useful extensions, but they are not necessary for most of the business projects in this course.