The development of large language models has led to a shift in the way developers approach AI applications, with a focus on creating agents that can reason, adapt, and take action. However, this shift has also created challenges, as models become increasingly probabilistic black boxes, making it difficult to debug and inspect their behavior. Langflow is an agentic model system that addresses these challenges by providing a modular approach to agent design, allowing developers to build atomic tools and functions, combine them into purpose-built agents, and use orchestration to route tasks to the right agent at the right time. This approach enables scalability while maintaining control and visibility, making it easier to debug and evaluate the behavior of the model. Langflow also provides a visual interface for building agents as tools, creating nested agent chains, and routing tasks based on context, mirroring both software engineering best practices and human collaboration patterns.