How AI Agents Work: Context Windows, Chunking, and Embeddings Explained

AI agents act as personalized guides that retrieve relevant information directly in response to user queries, rather than requiring users to search through data themselves. Because large language models can only process a limited amount of text at a time — known as a context window — large documents must be broken into smaller chunks before being analyzed. These chunks are then converted into vector embeddings, which capture the semantic meaning of text so that related concepts are stored closer together in a vector database. This approach allows the AI agent to efficiently retrieve only the most relevant chunks when answering a query, instead of scanning an entire dataset. Tools such as LangChain's PyPDFLoader and RecursiveCharacterTextSplitter, combined with embedding models, are commonly used to implement this pipeline in practice.