Learning guide
How much Python do you need to build AI agents?
You need enough Python to understand how information moves through a programme before building an AI agent you can troubleshoot.
You can start experimenting before becoming an advanced developer. A useful foundation includes functions, dictionaries, JSON, exceptions and basic testing. Your project will also need clear rules for which actions it can take.
This guide focuses on learning to build simple agents. It is separate from checking the prerequisites of a particular course.
What does an agent add to an ordinary model call?
In LangChain’s documentation, an agent uses a model to call tools in a loop until a task is complete.
That introduces a programming challenge beyond sending a prompt and printing an answer. Your application needs to interpret a proposed action, validate its inputs, execute the permitted operation and decide what happens next.
You should be able to follow that process in your own code.
The Python skills to learn first
| Skill | Why it matters | Practice task |
|---|---|---|
| Functions and arguments | Tools need clear inputs and outputs | Write a function that looks up a product |
| Lists and dictionaries | Requests and results contain structured information | Filter a list of product records |
| JSON and file handling | Applications exchange and store structured data | Read and validate a local catalogue |
| Exceptions | Tools and external services can fail | Handle a missing file or unknown identifier |
| Modules and dependencies | Projects need a manageable structure | Separate tools from the main programme |
| Basic tests | Tool behaviour should be checkable | Test valid, missing and invalid inputs |
Harvard’s CS50P is one starting option for programming beginners. Its published syllabus includes functions, conditionals, loops, exceptions, libraries, tests and file handling.
If you already programme in another language, the official Python tutorial may suit you better. It explicitly assumes general programming knowledge.
Build the tools before adding the model
Begin with an ordinary Python programme that reads a fictional product catalogue.
Write two functions: one that searches by product name and one that checks stock using a product identifier. Decide what each function returns when there is no match.
Test those functions directly.
Only then introduce a model that can request one of the available functions. Your programme should validate the request before running it.
This sequence makes it easier to distinguish a problem in your tool code from a problem in the model’s choice of action.
Learn to manage state and stopping conditions
A multi-step application needs a record of what has happened.
For a first project, a simple structure might contain the user’s question, completed tool calls and their results. You should be able to inspect it and explain why the next step occurred.
Set a maximum number of steps. Define what the programme should do when a tool fails or the available information is insufficient.
These are useful design choices to practise before adding more tools.
Do you need advanced maths, async Python or a framework?
Our suggested first project uses an existing model through an API and a small number of local functions. It does not require you to train a model.
Learn additional maths when your project or chosen course requires it. Learn asynchronous programming when concurrent operations become relevant.
A framework may reduce the amount of integration code you write. You will still need to understand the functions, data and decisions underneath it.
A sensible first agent project
Build a read-only catalogue assistant using fictional data. Allow it to search products and report stock, but give it no ability to place orders or modify files.
Test an unknown product, an invalid identifier, a failed lookup and a request outside its purpose.
These are our suggested exercises, not assignments from a provider.
Once you can explain the results and diagnose failures, explore the free LangChain Academy courses or our Hugging Face courses review.
For course-specific preparation, see our separate Eden Marco readiness checklist.
Course information checked 26 October 2026. Sources: LangChain agent documentation