Coding agents have quickly become one of a developer’s most important tools. They read code, build new features, and speed up development dramatically. So what is it actually like to work with them – and how does it change the developer’s job?
Agentic programming is starting to reshape how software gets built. New coding agents keep appearing, including tools from the biggest players:
As more companies hand these tools to their teams, the same questions keep coming up:
We’re well past the honeymoon phase – several months into using Claude Code daily. Here’s how those questions look from where we sit.
A coding agent is just a program that runs on your machine. Some come as desktop apps, like Antigravity or Claude Desktop; others live in the terminal.
The first thing you do is point it at the code it will work on. Terminal tools default to whatever directory you launched them from. Desktop apps usually let you pick a folder through a standard file dialog – the same kind you’d use to open or attach a document.
At that point you could technically start giving orders. But with most agents it pays to initialize the workspace first. In Claude Code, that’s the /init command. Run it, and the agent reads through your files to learn the project’s structure, its tech stack, and how the code is organized.
/init
The output is a CLAUDE.md file, where the agent notes the important things about the project: the technologies in use, its read on the architecture, the folder layout, and anything else worth keeping handy. Take a minute to check it – make sure the agent’s description actually matches your codebase.
CLAUDE.md
Now you’re ready to work. So far we’ve only covered setup; the real question is what working with an agent looks like in practice.
Modern coding agents usually run on a multi-agent architecture. When you send a command, it doesn’t go straight to a single model that does the job. It’s picked up first by a supervising agent – the orchestrator – whose job is to interpret the request, plan the work, and coordinate everything that follows.
What happens next depends on the tool and the task. Sometimes the agent maps the project structure; sometimes it hunts down the right files, runs tests, or checks its work against specialized sub-agents. But whatever the details, the overall loop tends to look the same:
It reads your command and pinpoints the parts of the project the task touches.
It reviews the relevant source code. If the feature already exists, it works out how it behaves. If nothing matches, it recognizes that something new has to be built.
Based on the information it has gathered, it plans how to carry out the task. Among other things, it analyzes what changes need to be made, where they should be made, how they will affect the rest of the system, and what risks or problems might arise.
If the agent has enough information, it proceeds to modify the code. When it encounters ambiguities or the need to make design decisions, it comes back to the user with additional questions. Once these are clarified, the process starts again from analyzing the new command.
Under the hood it’s messier than that. The orchestrator might handle the analysis itself, or spin up a sub-agent for one slice of the problem. The coding can be split across several agents at once. You can even kick off background research while another agent handles work you’re already confident about. From your seat, it feels less like using a tool and more like running a small team: you hand out tasks, then react to what comes back, judge it, and assign the next round.
The answer might surprise you. For the developer, the change is huge. For the development process itself, it’s barely changed at all.
Start with how programming work has always flowed. A ticket lands in your project tracker describing a bug to fix or a feature to build. The quality of that ticket varies from team to team, but the work that follows rarely does. First you understand the requirement and figure out which part of the system it hits. Then you find the code responsible – or the spot where new code belongs. You work out a solution, anywhere from a one-line fix to a whole new component. You write it. Then you test it. If something breaks, you loop back – sometimes to the requirements, sometimes to the design, sometimes just to the code. Software development has always been a loop of iterations closing in on the result you want.
That’s simplified, obviously. On real projects these stages get split across people, run in parallel, and repeat many times over. But whatever the scale, five phases stand out:
Understand → Find → Solve→ Write → Test
Then it all repeats until the result is good enough. It’s no accident that these phases map almost exactly onto how coding agents operate – not because the agents are imitating us, but because this is simply how software problems get solved. That’s why modern agents are built to move through the same cycle: analyze, plan, implement, verify.
If you use an agent like Claude Code, you stop being the one who writes every function, class, and loop. You hand chunks of that off and spend your time steering the process and judging the output. Responsibility moves from implementation to decision. The question is no longer “can you write this code?” but “does this solution actually meet the business requirement, fit the architecture, and avoid creating new problems?”
That might sound like the easier job. If the agent can read code and write it, why not just let it run? Because – for now, at least – that’s a gamble. Language models are improving at a startling rate, but they’re still not infallible. They need supervision and their decisions need checking. And even a huge context window can’t stand in for the product knowledge a developer accumulates over months or years.
So the role shifts from executor to AI supervisor. That’s a lot more than “type a prompt, glance at the result.” A tool like Claude Code produces wildly different results depending on who’s driving. You can run several agents in parallel on independent tasks. You can put one on research while another writes the implementation. A good developer knows which tasks can safely run at once and how to make the most of the resources on hand. Testing gets more room, too: while the agent builds the next piece, you can be verifying the last one or lining up what comes next.
The agent does what you ask. But organizing the work and making the calls is still on you. The danger is letting AI-assisted development become an excuse to coast – and a good developer always has something worthwhile to do while the agent runs.
Get to know Productive24AI! Contact us and we’ll gladly show you what the system can do.
Yes – no question. We’re shipping several times more features, and prototyping has gotten absurdly fast. We can now try out a handful of approaches before committing to the one that fits users best.
A test that used to eat 2–3 days – comparing different ways to build a feature – now takes tens of minutes. The payoff is enormous.
That doesn’t mean it’s frictionless. There are still places where the agent can’t grasp the full business or technical context on its own and needs steering. Architectural calls often rest on factors it doesn’t know or fully understand. It can lay out the options, but providing the context and making the final decision is the developer’s job.
Agents make mistakes. And there’s a real temptation to let them shoulder the blame for code quality – which, on our team, is a non-starter. That’s the reason for the rules.
The main one: every change that reaches the production version of Productive24AI is reviewed by a human. Agents can generate as much code as they like, but nothing merges into the main branch until a developer has read it and signed off.
Worth remembering, too: bugs didn’t arrive with AI. Programmers have always made them. If there are more issues to catch now, it’s mostly because we’re producing more – more code and more features in less time means more to check. That’s arithmetic, not a flaw in the tools.
The fix isn’t to drop AI; it’s to have solid review, QA, and testing in place. And here’s the good part: AI speeds things up enough that we can spend more time verifying and testing and still come out more productive than the old way.
Probably not. In today’s human-in-the-loop setup, a person is still needed at every step. What’s changing is the role – and which skills matter most. The premium now is on adapting to new tools, juggling several threads at once, and framing problems well. The thinking moves up a level: knowing the exact behavior of one function matters less; understanding the whole system matters more.
None of this is news to developers. Climbing to higher levels of abstraction is the oldest story in the field – from punch cards, to low-level programming, to today’s multi-paradigm languages. Programmers have always had to adapt, and right now agentic programming looks like a strong candidate for the next dominant approach. Adaptability was always valuable; at this pace, it’s becoming the whole game.
Still, it’s worth staying humble about forecasts. Models and AI tools are moving so fast that nobody can say with any confidence what the field looks like in a few years. The thing that makes today’s agents look quaint could land sooner than anyone expects. In AI, a few years can change everything – and sometimes a few months are enough.
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