OpenAI's release of the Assistants API marked a significant advancement from providing LLM APIs to offering Agent APIs, emphasizing the development of agentic infrastructure for running agentic applications. While the API introduced helpful features like configuring assistants with prompts, managing state, and running assistants as background processes, it limits the creation of complex cognitive architectures necessary for differentiated and reliable agentic applications. The API's generic approach restricts developers from easily building application-specific cognitive architectures, which are crucial for innovation and differentiation. Despite these limitations, OpenAI successfully highlighted the need for agentic infrastructure, making it easier for developers to manage tasks like state storage. LangChain aims to address these challenges by combining agentic infrastructure with LangGraph's control over cognitive architectures, allowing developers to focus on differentiation while outsourcing infrastructure tasks to platforms like LangGraph Cloud, which offers scalable and fault-tolerant solutions. The overarching message is that developers should focus on cognitive architectures, akin to improving the unique value of their applications, rather than solely on the infrastructure.