Agent memory
The mechanisms by which an AI agent retains information across steps or sessions, ranging from short-term working context to durable stored knowledge.
In more detail
By default a model starts each call with a blank slate beyond what is in its context window. Agent memory is the set of mechanisms that give an agent continuity: short-term memory holds the working state of the current task, while longer-term memory stores facts, preferences, and past results the agent can retrieve later.
Good memory design is mostly about relevance, not volume. An agent that carries forward too much becomes slow and distracted; one that carries too little repeats itself and loses context. The useful pattern is to store durably and retrieve selectively, bringing back only what the current step needs.
Where this shows up at Ceven
Ceven maintains the context a workflow needs to run coherently across its steps, and can draw on the customer's own knowledge and prior results where a step calls for it. The durable record of what an agent did lives in the audit trail, so past runs are not just remembered by the agent but reviewable by a person.