Emergency big update for open ai, i worked hard for you

Subject: Confidential discussion request — graph-native memory architecture for AI agents

Hello OpenAI team,

I am Jeffrey Cellauro, founder of Funesterie.

Over the past six months, I have been developing a working architecture for long-term AI agent continuity. The system combines graph-native memory, semantic indexing, artifact lineage, runtime orchestration and agent context reconstruction.

At a high level, the method uses Neo4j not as a knowledge dump, but as a relationship layer for AI agents. Raw content stays in its original location, while the graph stores why it matters, what it connects to, which agent used it, which decision produced it, and how future agents can reconstruct the right context.

The architecture also includes a runtime loop around task queues, planning, execution, tool dispatch, semantic indexing, compressed artifact continuity and graph feedback.

I believe this approach may be relevant to OpenAI’s work on persistent memory, agent orchestration, tool use, context reconstruction and long-running autonomous workflows.

Because this represents several months of original work and implementation, I would prefer not to send full technical details through an open or non-confidential channel. I can share a sanitized overview first, and then provide deeper technical material, demos or architecture details under an appropriate confidential or partnership framework.

The key principle of the method is:

Do not make the model reread the world. Give it a graph of why the world matters.

I would be happy to discuss whether there is an appropriate research, developer relations, partnership or confidential review channel for this.

Best, Jeffrey Cellauro Funesterie