Most agent memory systems fail because they treat memory like magic instead of infrastructure. This article breaks memory into four layers: state, retrieval, long-term memory, and procedural memory, and shows why those boundaries matter in production.
Some problems are only impossible because assumptions have quietly become constraints.
Valid xAPI statements are not enough. This article shows how to instrument video, assessments, and branching scenarios with consistent verbs, stable IDs, and one registration so learner behavior stays reconstructable later.
Short-term state is not a transcript. It is the working memory of the current task. This article explains how clean, structured thread state improves agent focus, reduces context pollution, and makes tool-using systems easier to debug and scale.
Tool use is where an agent stops generating text and starts affecting real systems. This article explains why tool design acts as both decision boundary and action contract, and how better schemas, validation, and tracing make tool calling agents more reliable.