Agentic coding represents a shift from AI as a suggestion engine to AI as an autonomous collaborator. Unlike traditional code completion tools that predict the next few tokens, agentic coding systems receive a high-level task description and independently break it into subtasks, write code across multiple files, run tests, interpret errors, and iterate until the task is complete.
The key differentiator is the feedback loop. An agentic system doesn’t just generate code and stop. It executes that code, observes the result, and makes corrections. This loop can run multiple times without human input, which means the agent can handle tasks that would require several rounds of copy-paste in a traditional LLM workflow.
Tools like Claude Code, Cursor, and Codegen operate in this paradigm, though they vary significantly in how much context they can access and how much autonomy they exercise.