How AI Coding Agents Actually Work Behind the Scenes
AI has fundamentally transformed how we write software. Just a few years ago, prompting an AI to “write a React component” felt impressive. Today, developers can hand an AI coding agent a much larger, multi-step objective:
“Find why the checkout API is returning a 500 error, fix it, add a test, and make sure the existing tests still pass.”
Once given this instruction, the agent doesn’t just generate static code—it acts. It navigates the codebase, analyzes relevant files, reads the existing implementation, applies targeted changes, and runs the test suite. If a test fails, it inspects the error output, refines its approach, and iterates until the solution is robust.
While this workflow can feel like magic, it is actually governed by a highly structured, repeatable system. Modern AI coding agents achieve this by combining a large language model (LLM) with deep codebase context, specialized tools, sandboxed execution environments, and a continuous feedback loop.
For developers, demystifying this architecture is crucial. By understanding the underlying mechanics, we can answer an increasingly important question: What is actually happening under the hood when an AI coding agent modifies our codebase?
