Autonomous software engineering agents are leveraging large language models (LLMs) to automate complex development workflows, handling tasks like planning, coding, testing, and debugging with minimal human supervision. These agents combine LLMs with planning modules, tool interfaces, memory systems, and feedback mechanisms to reason about tasks, write code, interact with tools, and refine outputs. The key components enabling this autonomy include the LLM as the reasoning engine, a planning module that translates high-level tasks into executable steps, a tool interface layer that interacts with development tools, memory and state tracking to maintain context across long-running tasks, and a feedback loop that iteratively refines outputs based on results. Platforms like MonsterAPI provide infrastructure support for these agents, enabling fine-tuning, deployment, and task orchestration through services such as no-code fine-tuning, one-click deployment, function calling support, scalable runtime configuration, and iterative prototyping.