How do you like to work with AI coding agents?
by•
There seems to have two types of developers:
Human in the loop: Those who like to control the behavior of their agents as it works, looking at the context usage, reading reasoning blocks, and approving individual file edits.
Agent first: Those who prefer to review the output of agents, rather than individual actions, and run one or more sessions in parallel.
What type of developer are you when working with AI coding agents?
For context, when @Kilo Code launched their new @VS Code extension last week, with parallel agents, inline diff reviewer, and multi-model comparisons, some users actually wanted more control back.
Curious about the community's viewpoint.
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@fmerian Agent-first has been useful for testing ideas faster, but I like checking results afterward to avoid mistakes.
Feels like there’s a spectrum rather than two categories. I still prefer human-in-the-loop for architecture or reasoning-heavy tasks, but for repetitive workflows I’m increasingly comfortable letting agents run more autonomously.
Around Turgo workflows, the biggest factor has been reliability and predictability. Once an agent behaves consistently, supervision naturally decreases.
Earth.fm
Nice prompt — this is exactly the kind of question shaping how we’ll work in the next few years.
For me, the best way to use AI coding agents is as “collaborative engineers, not autocomplete tools.”
I usually split work like this:
I let the agent handle boilerplate, refactors, debugging, and exploring alternatives, while I focus on architecture decisions, constraints, and final review. The biggest productivity jump comes when you treat the agent like a junior dev that can execute fast but still needs direction and validation.
Also, iterative feedback loops work way better than one-shot prompts — small tasks, quick checks, then expand.
definitely agent-first, i want to be less and less in the loop
Kilo Code
@fberrez1 interesting! fun fact: when @Kilo Code launched their new @VS Code extension with parallel agents last week, some users actually wanted more control back (read: visibility), i.e. seeing the agents' thinking and better diffs before approval.
curious what's your level of permissions? the more, the better?
@fmerian I have my machine with no personal stuff on it, my agents run with all permissions allowed on it.
I use Claude Code heavily, both at work and for personal projects, but I always read through and review the code it produces. The main issue I've noticed with AI-generated code is scaling. It often writes code that scales poorly, or starts off performant but loses the thread after a few rounds of redesign, with sloppy updates creeping in. Data structure choices can also be questionable at times.
Kilo Code
@prad1 some people might feel too "zoomed out" from the work using a terminal like @Claude Code, while an editor-first UI could feel like a misfit when the majority of code is written by agents.
what's your setup?
@fmerian I mostly use the CLI since it's convenient to run directly on a repository. After Claude finishes updating the code, I use GitKraken to review all the changes, and if something needs closer scrutiny, I open Rider or whichever IDE fits the language. Most IDEs come with AI plugins and extensions these days, but I still prefer the terminal.
I’m leaning toward a hybrid setup: human-in-the-loop for architecture, risky edits, and anything that changes core behavior — agent-first for implementation, refactors, and well-scoped tasks.
The sweet spot for me is giving the agent enough autonomy to move fast, then reviewing the final diff and reasoning at the decision points rather than micromanaging every file edit. feels like where coding workflows are heading. 👀
people said the same thing about AI writing copy and now half the internet runs on it without a second look. trust builds with consistency not permission screens
I'm more of a hybrid, but I lean more toward a human-in-the-loop approach.