Structuring Raw Interaction Data in AI Agents using Weaviate Engram

AI agents generate a substantial amount of raw interaction data during operation. When developers store this data as an ever-growing context blob and pass it back to a Large Language Model (LLM) on every turn, it leads to structural failures within the application. This approach causes long-context degradation, which inflates computational costs, increases operational latency, and reduces the accuracy of the model's outputs. Weaviate Engram, now generally available to the public, is a fully mana...