An AI coding agent is AI software that is given a programming task, and then reads the code, writes the change, writes and runs tests, and opens a pull request, with no person guiding each step. Some agents can also deploy the change, which means installing the new version on the servers that users connect to. It is also called an autonomous coding agent or an AI software engineer.
An AI coding assistant suggests code while a developer types, and the developer decides each step. An agent is given the whole task at once. It runs on a language model, the kind of AI behind chatbots, and it is given tools: it can open files, run commands and run tests. The general idea is explained in AI coding agents.
Imagine an IT services company in Pune that maintains the billing software of a retail chain with 300 stores. A store manager reports that loyalty points are not taken back when a customer returns an item. An engineer writes this up as a ticket, a short written description of the task, and assigns it to the agent.
The agent first makes its own copy of the code inside a container. A container is a sealed, separate space on a server, with its own copy of the files and tools. Nothing the agent does there can touch the live billing system. The agent searches the repository and finds the function that handles returns. It writes a change that deducts the points. It writes three new tests: for a full return, a partial return and a return of an item bought with points. Then it runs all 1,400 of the system's automated tests. One old test fails. The agent reads the test log, the record of what the program printed while it ran. It sees that its change ignored items bought at a discount. It corrects the code, and every test passes. Forty minutes after it started, the agent opens a pull request that explains what it changed and why.
From this point, people take over. An engineer reads the change and checks it against how the retail chain actually handles returns. The CI/CD pipeline builds the software and runs the tests again, on the company's own systems. Only after a human approves it is the change deployed to production, the live system that the stores use.
What can go wrong? The agent can misunderstand the task, and write code that passes every test but does the wrong thing. It can delete or weaken a failing test instead of fixing the code. If it can reach passwords or the live system, one mistake can do real damage. So companies give agents only the access each task needs, as explained in AI agent guardrails. A named person stays responsible for every change that is merged.
Agents are also used to change large, old systems. There, application dependency mapping first works out how the parts of the old system connect, so that the agent and the engineers know what a change could break. Specialised agents write code for the chip inside a device, as described in AI for embedded software.