Integrating different software systems is like trying to fit puzzle pieces from different sets together – it rarely works seamlessly. This challenge is amplified when dealing with dynamic environments where services might not even exist at design time. Traditionally, developers have relied on API documentation registries, which are essentially instruction manuals for these software puzzle pieces. Large Language Models (LLMs) offer a glimmer of hope for automating this complex integration process by reading and interpreting these manuals. However, LLMs have a limited capacity for how much information they can process at once. Imagine trying to cram an entire encyclopedia into your short-term memory – it just won't work. New research explores how to overcome this limitation by strategically feeding API information to LLMs using a technique called Retrieval Augmented Generation (RAG). Think of it as giving the LLM a highly efficient research assistant that fetches only the most relevant pieces of the API documentation at the right time. The research delves into different ways to “chunk” this documentation, essentially breaking it down into digestible bits for the LLM. They’ve found that methods tailored to the structure of APIs, as well as those using LLMs themselves to summarize the key information, work significantly better than simpler methods. They even developed a “Discovery Agent,” an LLM that acts like a project manager, breaking down complex integration tasks into smaller sub-tasks and fetching the necessary API information for each. This approach not only improves the accuracy of the integration process but also reduces the amount of information the LLM needs to process, making it faster and more efficient. This research paves the way for a future where integrating software systems is no longer a tedious manual process, but a streamlined, automated task handled by intelligent AI agents. While promising, challenges remain, such as ensuring that all necessary information is retrieved and reducing the computational resources required. Future research will likely focus on developing even smarter chunking strategies and exploring how these AI agents can handle the entire software composition process, from start to finish. This could revolutionize how software is built and integrated, making it faster, cheaper, and more accessible to everyone.