Three critical components—tools, memory, and repository context—enable LLMs to function as effective coding agents. These systems move beyond simple chat by integrating repo context to understand project structures. This architecture reduces hallucination and improves code accuracy. Developers can now build more reliable autonomous workflows by prioritizing these specific structural elements over raw model size.