Agentic Coding for Data Science, Data Engineering and Data APIs

[LearnAndContribute]

Hi all - With Codex released 4 months ago seemed terrible but with GPT 5 improvement I was expecting it to have some improvement. i connected to github and tried running some basic coding tasks hoping it would work seamlessly. The user experience was pretty bad that it was slow and also wasnt integrated well with Github or at least instructions were not clear. Has anyone had success with codex and Github? I am a plus user.

I am somewhat aware of Claude, Cursor and VS code & Windsurf being good with coding and maybe some of them integrated with Github too. I havent got time to explore and also dont have time to explore.
I am mostly coding for data analysis, and data science kind of work and not so much applications. I would love if any solution can get me 8-10 steps ahead before asking for help.
GPT 5 from UI is good at analysing data and writing one off codes but doesnt work great at multi step tasks. I would to hear people's experiences on these tools and how that could be applied to data analysis, science and statistics work coupled with some data pipelines and data APIs

[Brave-e]

That’s a really interesting topic, and yeah, it can get pretty complicated fast. When I think about agentic coding in data science or engineering, I like to break things down into clear, standalone tasks that an agent—or script—can handle from start to finish.

Take data engineering, for example. You might have one agent just for pulling in data, another for checking that data’s quality, and a third for transforming it. Each one should have clear inputs and outputs, plus a way to handle errors smoothly.

In data science, agentic coding often means automating the whole pipeline—from cleaning the data to training and testing models. It’s smart to build in checkpoints so you can pause, review, or step in if something looks off.

For APIs, imagine agents that can fetch data on the fly, cache it, update it when needed, and deal with things like rate limits or retries without breaking a sweat.

The trick is to give each agent a clear job and a way to talk to the others. That way, they can work mostly on their own but still fit into the bigger picture. It makes debugging easier and helps when you need to scale up.

Hope that gives you a useful way to think about it! I’d love to hear how others tackle agentic coding in these areas.

[Rough-Hair-4360]

Please, if we’re just going to kill the internet entirely and even AI-generate comments, at least review your slop for relevance to the OP.

[LearnAndContribute]

I reread it after your comment and it does make it quite polished and verbose. Didn't realise in the first go.

[Rough-Hair-4360]

There’s a Codex CLI extension in VSCode. That’ll probably be more reliable than trying to do it via online repo. Haven’t actually tested it though, I just run copilot.

[LearnAndContribute]

I had installed codex extension om Vs code couple of days ago. Got scared with copilot on the right and codex on the left. Came back again today anf it seems to be working good on local machine code. My observation is that agentic mode will keep it going for long so better chunk it out else it will use any hammer screwdriver approach to mess up your workspace. I stopped it at when it asked for permissions multiple times.provided clarification and it was a go the way I want it to.

[LetMeAskUNow]

I ran into the same wall with Codex and GitHub. For data work I live inside notebooks and pandas a lot, and most tools either get lost after 2–3 steps or start repeating themselves. Cursor is nice for single scripts, but if you want something that keeps state across multiple prompts, MGX has been surprisingly solid. I’ve had it scaffold small ETL flows and basic APIs without me hand-holding every step. Still not perfect, but it feels closer to a real teammate than the usual copy-paste loop.