Software agents are emerging as powerful tools for tackling complex software engineering tasks. However, existing approaches often oversimplify development workflows, assuming basic models that lack the nuances of real-world software processes. Moreover, they frequently include entire codebases in their instructions, leading to inefficiencies when working with large-scale projects. To overcome these challenges, we introduce AgileCoder, a multi-agent system that incorporates Agile Methodology (AM) principles, assigning specialized agents to roles such as Product Manager, Developer, and Tester for collaborative, iterative development. AgileCoder structures work into sprints, enabling \textit{incremental} progress based on user input. A standout feature, the Dynamic Code Graph Generator, continuously builds a Code Dependency Graph as the codebase evolves, allowing agents to gain a deeper understanding of the structure for more precise code generation and efficient modifications. We evaluate AgileCoder on two fronts: (1) code generation benchmarks, including HumanEval and MBPP, and (2) real-world software development scenarios. The results show that AgileCoder outperforms existing systems like ChatDev and MetaGPT, setting a new standard for multi-agent systems in software engineering. The source code is available at https://anonymous.4open.science/r/AgileCoder-Submission.