Product memory, retrieval, and agentic workflows

Useful AI products need context that is organized, retrievable, and tied to the product workflow. Without that, the model is operating on whatever the prompt happened to contain at that moment.

Product memory can mean embeddings, semantic retrieval, structured state, operation history, and checkpoints. The exact architecture depends on the product, but the principle stays the same: the system should know what matters before it acts.

Agentic workflows add more pressure here. Planning, tool execution, retries, and resume state need reliable context, or the agent becomes difficult to trust.