If your AI agents reset every session, they are not learning. They are just repeating.

@lyonwj recorded a session on exactly this problem: why most agents are still stateless, what breaks in production as a result, and how memory and context graphs fundamentally change the architecture.

The full session also covers practical design patterns, the most common pitfalls when implementing memory, and integration approaches for Salesforce Agentforce, Databricks, and major cloud providers.

30 minutes. Watch on demand in the link: https://bit.ly/4dBPQ3h

Design stateful agents with memory and context graphs

Source: https://go.neo4j.com/WBR-EDU-260604-Memory-and-Context-Graphs_Registration.html?utm_source=linkedin&utm_medium=organic&utm_campaign=webinar 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.

Watch on-demand 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.

SPEAKER

Will LyonSenior Product Manager, Neo4j

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