  Multi-agent systems are often limited in terms of persistenceand scalability.
This issue is more prevalent for applications inwhich agent states changes
frequently. This makes the existingmethods less usable as they increase the
agent's complexityand are less scalable. This research study has presented
anovel in-memory agent persistence framework. Two prototypeshave been
implemented, one using the proposed solution andthe other using an established
agent persistency environment.Experimental results confirm that the proposed
framework ismore scalable than existing approaches whilst providing asimilar
level of persistency. These findings will help futurereal-time multiagent
systems to become scalable and persistentin a dynamic cloud environment.
