Many AI agents are still largely stateless. They hallucinate and produce inconsistent outputs, so you need to rebuild context on every request. While modern cloud-native agent orchestration services can enable reasoning and tool use, these platforms can’t advance the path from prototype to production without providing memory.

Persistent memory, combined with ontology and data, works within the Enterprise Knowledge Graph (EKG) to ground and scale intelligent systems. A knowledge graph can be extended with a context graph that includes decision outcomes and reasoning traces. Together, memory and context graphs can help you design stateful, production-ready agents.

Join us on 4 June to learn how context graphs manage agents’ real-time interaction states, while long-term memory enables continuity, learning, and personalization across sessions. See how the two work together in modern agentic systems. 

We’ll also cover design patterns, common pitfalls, and integration approaches across frameworks like Salesforce Agentforce, Databricks, and other major cloud providers. You’ll leave this webinar with insights on how to move from stateless demos to robust, real-world AI applications.