Effective communication is paramount in both onboarding new hires and building language model applications (LLM), where clear instructions and context are essential for reliable performance. Prompt engineering remains crucial as models advance, and the integration of code into the "cognitive architecture" of agents becomes vital for precise communication. Agent frameworks like LangGraph facilitate building customized applications by handling infrastructure concerns while maintaining flexibility in communication via prompts and code. The multidisciplinary nature of agent development benefits from the involvement of domain experts alongside engineers, enhancing the overall "cognitive architecture." Tools like LangSmith aid collaboration by visualizing data and simplifying the debugging process, crucial for understanding and improving agent behavior. The innovation in UI/UX is also emphasized as a key factor in enhancing human-AI collaboration, highlighting the overarching importance of communication in developing successful AI applications.