Distinguish short-term vs long-term agent memory and name a failure mode specific to each

An agent has two kinds of **memory**, and they sit in different places. The first is short-term memory. This is just the context window, the running prompt that holds the current task, the recent tool results, and the conversation so far. It is fast to read because it is already in front of the model, and it needs no extra system. But it is small and it disappears when the session ends. The second is long-term memory. This is an external store, usually a vector database, a regular database, or a set of files. The agent does not see it automatically. It has to run a retrieval step to pull the right pieces back into the context window when they are needed. The simplest way to remember the split is by location. Short-term memory is what the model can see right now. Long-term memory is what it has to go fetch. Each kind fails in its own way. Short-term memory overflows: once the task grows past the window, early steps fall out and the agent forgets what it already did. Long-term memory goes stale: a fact saved months ago can be wrong today, and the agent has no built-in way to know it has aged.

Agent memory splits by where the information lives and how the agent reaches it. **Short-term memory** is the context window. It holds the current task, recent tool calls, and the working scratchpad for this session. It is fast because no retrieval is involved, and it needs no external infrastructure. The cost is that it is bounded by the context limit and is wiped when the session ends. Long-term memory is an external persistent store: a vector database, a relational database, a key-value store, or plain files. It survives across sessions and is effectively unbounded. The agent reaches it through an explicit retrieval step that pulls relevant slices back into context on demand. The write policy follows from this. Put transient, this-task state in short-term memory. Promote durable facts, user preferences, and learned outcomes to long-term storage. Each type has a signature failure. Short-term memory suffers context overflow: when history exceeds the window, early steps silently drop and reasoning degrades. Long-term memory suffers stale retrieval: a fact true at write time is served today as if still current. The agent has no flag that the data has aged, so it cites the old value with full confidence.