Breaking the Manual Loop: The Rise of Self-Executing AI Agents

Language models excel at reasoning about code but cannot execute it, creating tedious manual feedback loops where users must repeatedly copy-paste results. The agent loop solves this by automating tool execution and feeding results back to the LLM, enabling truly autonomous agents that can read files, run commands, and verify results independently. This pattern is crucial as AI coding assistants evolve from passive suggestion engines to active problem solvers.