The Speed of Prototyping in the Age of AI

I work as a DevOps engineer and have been using agents exclusively to code since the beginning of the year. Agents are really nice to quickly craft utilities to speed up planning. For instance I had it create a small cli for me that'll pull my cards from azure DevOps, load them as json, markdown and csv, and push updates once I'm done. Then I'll load into context transcripts of meetings and other written requirements, cross with current state of repos, to have meaningfully conrextualized work items without me having to implement these myself. I'll just have a long chat with the agent exploring these cards and defining the necessary refinements for description and acceptance criteria than I jusr push them all at once. Anything you can think of you just ask for the agent, so for me I don't trust code, so I'll have all my clis be no-op by default, so they will first print all they'll do and if I think the changes make sense I approve them and let the script commit to the canonical board.

Working with cloud consoles like Aws in general is a huge hassle, so crafting quick inventory utilities and tools for correlating data is a breeze.

Now the work itself is mainly ci pipelines, terraform files and automation. For these I'll base the agents on the specified work items and enrich them with my own understanding of the problem. I then launch the agents and read the agent output attentively. This is very important. You can't just prompt and leave, you need to be present all the time so you can steer the agent into solving the right problems. At the very least you need to review all the changes after an implementation session is done when you came back from making coffee. Many times it tries to create meaningless abstractions or very complicated solutions that I know can be done better. Or I have a different idea of how to organize the project so I do many follow-up sessions to refactor code.

In my personal projects I do a lot of small utilities. I spent some weeks designing and polishing a replacement for zurg and debridmediamanager the way I like it to be, simple and to the point, also tightly integrating them with jellyfin https://gitlab.com/gabriel.chamon/buzz

I have my own micro desktop environment on top of hyprland called Archie which recently I've been redesigning and improving a lot with agents https://gitlab.com/gabriel.chamon/archie

I have my own agile based methodology for creating and managing work items with tight integration with gitlab https://gitlab.com/gabriel.chamon/orisun

I have been improving my fork if gamma-launcher so that installing and managing the game on bazzite is simpler and more automated than relying on workarounds for workflows intended for windows https://gitlab.com/gabriel.chamon/gamma-launcher

Now for how I approach developing with agents. I think it's really important to get your constraints sorted out as soon as possible, so have your agent create a CI pipeline for code quality testing, like with ruff, pyright and pytest, to control style, code consistency and cyclomatic complexity. Put in the AGENTS.md explicit instructions that the agent must run these tools at the end of every coding session. If adopting a new project, use the agent to explore the code and see which refactoring points are worth tackling. Agents really thrive on good codebases, so this first code quality improvement pass is a must.

To sum it up, with agents you give up writing code manually for reading lots of code, exploring the domain with the help of the agent and architecting the solution at a strategic level. You trust the agent but you also verify. And lots and lots of manual testing. My personal take is that I'm infinitely productive now, only constrained by how much code and agent terminal output I can read, and also by the rate limits of the model providers and mental fatigue.

Imagine if instead of f AI generated code, we all just started copying and pasting code from open source repos.

Imagine my velocity! I cloned the Linux kernel in seconds!

Instead we're basically doing exactly that, except through an AI remixer.

It leaves a very sour taste in my mouth

So you do no validation of the code that’s generated? Just asking because you didn’t state that as a step in your process. You’re prototyping to running then you’re missing a big step that will most likely cost you later.

Why does it matter how much time I spent writing code for a project I’m most likely either not sharing or if I am sharing it can be obtained for free? Which market am I rushing to? Bluntness doesnt seem to be an advantage other than bragging.