The world of software engineering is undergoing a seismic shift, thanks to the rise of large language models (LLMs). Initially, LLMs like GPT and Codex impressed with their code generation and bug detection abilities. But they weren't without limitations—context length restrictions, occasional "hallucinations" (generating incorrect code), and an inability to use external tools hampered their full potential. Now, the evolution continues with LLM-based agents stepping into the spotlight. These agents combine the language prowess of LLMs with the dynamic power of external tools and resources. Think of an LLM as a brilliant coder, and the agent as its resourceful manager, providing the right tools and information at the right time. This shift enables agents to tackle more complex, context-aware tasks. Instead of simply generating code snippets, they can autonomously debug, refactor, and even generate adaptive test cases that evolve with the codebase. This survey paper explores the exciting transition from LLMs to agents across six key software engineering areas: requirement engineering, code generation, autonomous decision-making, software design, test generation, and software maintenance. The research digs into the differences between LLMs and agents, comparing their tasks, benchmarks, and evaluation metrics across these domains. One key takeaway is the increasing autonomy of LLM-based agents. While LLMs excel at specific tasks, agents can orchestrate entire workflows, dynamically choosing the right tools and strategies. Imagine an agent autonomously managing a software project, assigning tasks, and ensuring code quality – this is the future LLM-based agents are building. The survey also highlights the challenges of evaluating these sophisticated systems. Traditional metrics like precision and recall are still relevant, but new measures are needed to capture the agents' dynamic and collaborative nature. How do you measure an agent's ability to adapt and learn? This is a key question researchers are grappling with. The shift from LLMs to agents marks a significant leap towards automating and optimizing the software development lifecycle. While challenges remain, the potential of these intelligent agents to revolutionize software engineering is undeniable. As researchers continue to refine these agents, expect to see even more sophisticated and autonomous systems shaping the future of software creation.