Coding Agent

A coding agent is a computer program that, with the help of artificial intelligence, independently takes on tasks from software development. You describe a goal to it in plain language, for example: “Fix the login error.” The agent then works through many small steps. It looks at the existing program files, changes them, runs the program, and checks whether it works. If something doesn’t work, it tries a different approach. The crucial difference from a normal chatbot: the agent doesn’t just talk about the solution, it also carries it out.

From autocomplete to independent coworker

The first generation of AI aids for programming worked like a better autocorrect. You typed a line, and the tool suggested the continuation. The human always remained in control and checked every suggestion immediately. A coding agent shifts this boundary considerably. It is allowed to complete entire work packages on its own and only reports back with a result.

Economically, this is the reason for the great interest in these tools. A significant portion of the work at software companies consists of routine tasks: writing tests, upgrading old libraries to new versions, hunting down recurring bugs. Such tasks are easy to describe and easy to verify. That is exactly where agents are strongest, and exactly where companies expect savings.

At the same time, a new risk emerges. If a program independently modifies files or executes commands, it can also cause damage independently. That’s why agents usually receive only limited permissions, and a human must approve the changes. Experts call this control step before adoption “review.”

The loop of planning, executing, and checking

At its core is a language model, an AI system that continues text and, in doing so, can also generate program code. On its own, it could only output text. The agent additionally gives it tools: read files, write files, run commands in the terminal, search the project for a word. The model decides at each step which tool it wants to use. It receives the result of that tool back and plans further with it.

This repetition is the heart of the matter. The agent formulates a plan, carries out a step, reads the result, and adjusts the plan. You can picture this like an intern with a logbook. He notes down what he tried and derives the next attempt from it. If a test passes, the step is considered done. If it fails with an error message, that message becomes the next clue.

Two limitations are typical. First, an agent can only keep a limited amount of text in view at once; a very large project doesn’t fit in completely. So it has to look things up selectively instead of reading everything. Second, it can get stuck: it goes around in circles without real progress. That’s why it is given a budget of steps or computing cost, after which it stops.

Where coding agents are in use today

You encounter them most often in development environments, i.e., the programs in which software is written. Well-known examples are Cursor, Claude Code, GitHub Copilot in agent mode, and OpenAI Codex. Some run directly on the developer’s machine, others in the cloud, meaning on rented servers on the internet. There, they work on tasks while the human does something else.

In business news, coding agents come up from two directions. On one hand, as a growth story: companies like Anthropic, OpenAI, or Cursor report rapidly rising revenues from such tools. On the other hand, as a question about jobs, especially for entry-level programmers. Both debates remain open; solid long-term figures are still lacking.

A common misconception is that an agent replaces the understanding of software. In practice, the work shifts instead: less typing, more precisely describing tasks and checking results. Anyone who fails to recognize that a generated solution is poorly built will only notice the error later. This is best tried out on a small personal project where the agent is asked to write tests.