The evolving landscape of large language model (LLM) applications necessitates designing APIs specifically tailored to meet their unique demands, emphasizing efficient and predictable interactions that accommodate the probabilistic nature and context reliance of LLMs. The effectiveness and scalability of LLM apps are heavily dependent on robust API design, which facilitates seamless interaction with external systems and data sources. APIs serve as a critical communication layer, enabling LLMs to perform grounding and action functions, transforming them from simple text generators into capable agents. Key considerations for AI-ready APIs include semantic clarity, contextual awareness, granularity, robust error handling, and support for asynchronous operations. Additionally, tools like OpenAPI and JSON Schema play an essential role in defining and validating API structures, ensuring data consistency and predictability. Effective API design minimizes friction, enhances task performance, and unlocks the full potential of LLM-powered applications while addressing common pitfalls such as ambiguous responses, lack of context management, and security risks. As AI ecosystems evolve, APIs must adapt to remain a strategic asset, ensuring the reliable and scalable performance of LLM applications.