Agentic Coding
Agentic Coding refers to a form of software development in which an AI does not merely suggest individual lines of code, but independently plans, creates files, runs tests, and fixes errors – until a task is completed. The human sets the goal; the AI chooses the path.
With a classic AI coding assistant, you type a line, the AI suggests the next one, and you decide whether to accept it. Agentic Coding goes a big step further. You describe a task in plain language — for example, “Build me a login feature with email confirmation” — and the AI works through this task independently: it creates files, writes code, runs automated tests (i.e., programs that check whether the code runs correctly), and fixes any errors that occur along the way. This process runs over multiple rounds without requiring intervention after every step. The word “agentic” comes from the English “agent,” meaning an actor who acts on their own toward a goal — in contrast to a tool that only reacts to direct commands.
Why Agentic Coding is changing development
Until now, programming was a craft that consumed a lot of time on small tasks: writing boilerplate code (i.e., recurring standard building blocks), hunting for bugs, integrating libraries, reading test results, and adjusting the code. All of this is time-consuming but not particularly creative. Agentic Coding takes over exactly these layers.
This changes what developers actually do. They spend less time writing syntax and more time formulating requirements, reviewing results, and making architectural decisions — that is, deciding how a system should fundamentally be structured. Some companies report that individual developers using Agentic Coding tools accomplish work that previously required small teams. This sounds impressive, but it also carries a serious downside: those who don’t understand the generated code may notice errors only very late.
How an AI agent navigates coding tasks
At its core, Agentic Coding runs as a loop. The AI is given a goal and a description of the current situation — which files exist, which error messages are present. It then chooses an action: writing code, reading a file, running a command in the terminal, or starting a web search. The result of that action immediately flows back, and the AI decides what comes next. This loop repeats until the goal is reached or the AI gets stuck.
The crucial ingredient is what are known as tools — interfaces through which the AI actually interacts with the outside world: the file system, terminal, browser, or external services. Without these tools, it could only generate text but not execute anything. With them, it can control an entire development environment. Modern systems also rely on multiple specialized AI instances working in parallel: one plans, one writes code, one runs tests. This significantly speeds up complex tasks.
A common misconception is to equate Agentic Coding with simple autocomplete. The difference is fundamental. Autocomplete reacts to the last keystroke and suggests the next few characters. A coding agent plans dozens of steps ahead and makes independent decisions that affect the entire project.
Agentic Coding in products and in the news
The best-known example is GitHub Copilot Workspace, which Microsoft introduced in 2024. Developers describe a problem there in a GitHub issue (a kind of task card), and the system independently creates a plan, writes the code, and opens a finished change proposal. Similar approaches are pursued by Cursor, Devin from Cognition, and Amazon's internal AI service for the AWS development environment.
In tech news, Agentic Coding usually comes up in connection with two questions: How many developer jobs will become obsolete? And how safe are systems in which an AI independently executes code and modifies files? The second question is technically more pressing. An agent that executes commands unsupervised can multiply errors at a pace a human can no longer keep up with. That’s why most professional tools rely on so-called human-in-the-loop mechanisms — that is, checkpoints where a human must first approve the next step.
For newcomers to programming, Agentic Coding is a double-edged offer. You can achieve working results quickly without understanding every detail. But that is exactly the risk: anyone who cannot judge whether the generated code is safe and correct should familiarize themselves with the fundamentals before handing the wheel over to an agent.