The most successful implementations of AI applications were found to be those that use simple, composable patterns rather than complex frameworks or specialized libraries. These simple patterns can be combined to build solutions that exactly match the needs of a particular application, making it easier to understand and maintain the system. In contrast, many popular AI tooling frameworks like LangChain introduce layers of abstraction that make systems harder to debug and customize. The author suggests that these abstractions create unnecessary complexity and technical debt, leading to harder-to-maintain systems. Instead, tools like LiteLLM exemplify good AI tooling by solving a single well-defined problem with a unified interface for LLM provider APIs. By focusing on building simple, focused components that can be combined to meet specific application requirements, developers can create sustainable and maintainable AI solutions.