Agile development， with its iterative and rapid delivery characteristics， has become a mainstream software development mode. However， it still faces challenges such as unsustainable evolution， difficulty in knowledge retention， and collaboration issues caused by skill disparities. With the rise of Large Language Models （LLMs） and agent-based technologies， an agile enhancement development framework driven by human-multi-agent collaboration was proposed with LangChain as the collaborative core and Retrieval-Augmented Generation （RAG） as the knowledge support. The framework was optimized through three core mechanisms： an architecture design Agent was introduced to generate three differentiated architectural schemes — conservative （adapting to current requirements but requiring long-term refactoring）， innovative （supporting complex， future-oriented implementations）， and balanced （mediating between current and future needs） — which were evaluated using a seven-dimensional quantitative assessment （including scalability） to assist human architects in decision-making， thereby addressing the challenges of insufficient modeling of technical debt sources and predicting architectural evolution. Development artifacts were automatically generated through agents and predefined templates. Role-based modeling， grounded in a knowledge base， defined the functional boundaries and execution standards of agents， precisely matching tasks with capabilities to reduce collaboration bottlenecks. Experimental results on a portal website project demonstrate that， compared with traditional Scrum methods， the proposed framework reduces development cycle time by 29.6%， decreases defect density by 46.9%， and improves user satisfaction by 16.5%， effectively validating its engineering value and promotion potential in enhancing automation and knowledge management， while ensuring delivery efficiency and system quality.