Where this offering fits
AIStor Memory addresses persistent organisational memory and context for AI agents. It is a different requirement from holding model-training files alone. Begin with the information an agent may retain, who may reuse it and the lifecycle rules for correcting or removing that information.
Architecture and rollout decisions
Map agents, applications and memory consumers, then confirm current supported interfaces. Use synthetic interactions to test retention, retrieval and isolation between authorised audiences. Review secrets and personal information boundaries before evaluation. The acceptance plan should distinguish relevant recalled context from incorrect or stale memory and demonstrate the approved correction or removal process.
An evaluation scenario for this offering
Create two fictional agents with distinct approved memory scopes and provide each with synthetic facts, including one fact deliberately updated later. Ask questions that require recalling relevant context, then check that one agent cannot retrieve the other’s restricted information. Exercise the documented correction or removal workflow and observe subsequent retrieval. Include an outdated statement to see how the consuming application handles conflict rather than assuming storage decides truth. Preserve source facts, permissions, retrieval results and lifecycle actions. Acceptance should establish the supported memory behaviour within this application, not imply that persistent context makes an agent’s conclusions inherently reliable.
Evidence to request for this scope
- Confirm the current product architecture and integration support.
- Test separation of memory across authorised agent scopes.
- Define retention, correction and retrieval-quality evidence.