Coding agents use repository context, tools, and memory to boost LLM performance. By pulling relevant code snippets from the repository, the agents reduce hallucinations and improve code quality. Developers can rely on these agents to write accurate code faster. The approach integrates tool calls, short-term memory, and contextual code search, giving the LLM a focused knowledge base.