What is Agent Memory?
Agent memory refers to the mechanisms that allow AI agents to store, retrieve, and use information across interactions and sessions — including conversation history, learned preferences, and accumulated knowledge.
WHY IT MATTERS
Without memory, every agent interaction starts from zero. The agent doesn't know what it did five minutes ago, what the user's preferences are, or what happened in previous sessions. Memory makes agents useful over time.
Agent memory typically comes in several forms: short-term (current conversation context), working memory (structured state for the current task), long-term (persisted across sessions via vector databases or key-value stores), and episodic (specific past experiences the agent can recall).
For financial agents, memory is critical for continuity. An agent managing a portfolio needs to remember its positions, past trades, cumulative spending, and risk parameters. Without proper memory, it might repeat transactions or violate cumulative limits.
HOW POLICYLAYER USES THIS
PolicyLayer maintains its own record of agent spending history — independent of the agent's memory. Even if an agent's context is reset or its memory is corrupted, PolicyLayer tracks cumulative spending accurately to enforce rolling budget limits.