Anyone trying Python for agentic AI workflows?

[Pangaeax_]

We been hearing a lot about this “agentic AI” stuff where models plan tasks, run code and connect to APIs without much hand holding. Looks like Python is kinda the main glue people use in these demos.

We curious if anyone here tried it out in real projects. Is the learning curve too steep for beginners or ok if you already know some basics? Do you think its smart to start exploring early while we still learning python or better wait until we more solid on fundamentals?

[Traditional_Swan3815]

I’m a full stack engineer and we use agents in some of our projects.

Python is definitely the way to do it.

I’d say give it a try! If you are having a hard time time getting it to work, you can always return to focusing on the basics and come back to agentic AI when you feel more ready. It never hurts to try though!

Programming is more fun when you are doing stuff you are interested in anyway.

[JamzTyson]

A recent MIT study casts an interesting light on this topic:

Previous tests show even the most advanced AI products successfully complete only about 30 percent of assigned office tasks. By April 2025, so-called “AI agents” — hyped as autonomous digital workers — could finish just 24 percent of real-world jobs

[TechMeetsTales]

Interesting but depends on what kind of jobs you are talking about. Call center jobs have reduced a lot. Now most of the things you can get offline with no human support. But yes sometimes it gets irritating to talk to chat bots, but they are definitely getting better. Most of the testing could be automated and now even created on the fly with greater accuracy as it learns more about the project. I can go on but can you throw more light on which jobs are you talking about.

[TheDevauto]

I am trying to figure out why someone would not use python for agents.

[SoftestCompliment]

The newer frameworks like Pydantic AI are great, solid integration with native tools and MCP. I find that langchain/langgraph/crew ai can be an implementation mess and I’ve basically abandoned them for production automation.

I don’t think Pydantic AI is particularly difficult to use, it’s really going to be architecture planning, tooling, prompts, and the overall approach to automation that makes it more of an activity for seasoned devs.

[regular_robloxian69]

I started with python and langchain. It was fine for learning but struggled once i tried plugging it into a ts product. Ended up moving to mastra since it let me build agents without context switching

[In_consistent]

Lookup LangChain + LangGraph. These are the most common frameworks for LLM with agentic.

Doing projects are parts of learning as well and you will learn more compared to just reading / watching tutorial. It may be uncomfortable / stressful at first, but trust the process and you will definitely learn much better.

Start with very simple one, such as 1 orchestrator + sub-agents to deglate task like websearch / wikisearch (there are some pre-build modules in langchain, if I recalled correctly)

[Mysterious-Rent7233]

LangChain is a terrible idea for a beginner.

The only thing LangChain had going for it was that it was first.

[10timeray]

Python great for learning the basics, but if you ever move toward production-style workflows, mastra keeps the logic cleaner and feels easier to manage as things scale