I’m a developer from Costa Rica, and I’ve been working on Eon, an experimental AI architecture that moves away from the traditional ‘reactive’ prompt-response model. My goal was to create a system that feels more like a continuous flow of thought rather than a static tool.
What makes it different? Most LLM implementations rely on a limited context window and lack true persistence. Eon uses a multi-layer agent architecture where specialized micro-agents collaborate and validate information before delivering it. It’s designed to build a dynamic knowledge graph that evolves with every interaction.
Why I’m building this: In my local tech scene, there isn't much support for deep AI R&D, but I believe the future of AI isn't just bigger models, but smarter architectures. I’ve focused on:
Heuristic Self-Correction: Reducing hallucinations by auditing logic in real-time.*
Modular Memory: A way for the AI to "remember" and relate concepts across different sessions without bloat.*
Event-Driven Execution: To keep it efficient and scalable.*
The project is still in development, and the code is open on GitHub. I’d love to get feedback from this community on the architectural approach and how to better manage the state between these agents.
Repo: https://github.com/jeremy-sud/Eon-AI-Project
Thanks for taking a look!”