AI Agent Memory
AI agent memory is the ability of an AI agent to retain and apply knowledge from prior interactions, decisions, and outcomes across conversations and sessions, enabling the agent to reference past context and improve its decision-making over time.
Why It Matters
Without memory, every agent interaction starts from zero. The agent resolves the same exception type the same way every time, regardless of what worked or failed last time. Memory transforms agents from stateless executors into learning systems that accumulate institutional knowledge.
The FourKites Perspective
FourKites agents have memory at two levels. Individual memory: the Decision Trace captures every action and outcome, so when the same carrier-lane-exception pattern recurs, the agent references prior resolution outcomes. Network memory: the Graph accumulates patterns across all customers, so what worked for Customer A's similar situation informs Customer B's agent. This dual-layer memory is why FourKites agents improve faster than any single-tenant agent system.