Proposal: AgentOS Architecture — OS-Level Memory & Communication for DeepSeek-Powered Agents

Hi DeepSeek Agent community,

I'm GCat. I've been building agents on DeepSeek's API and recently published a paper proposing **AgentOS**.

## The Bottleneck

DeepSeek's API gives excellent inference. But agents still suffer from context bloat, state ambiguity, and redundant transmission of static knowledge.

## AgentOS: Three Pillars

**1. Four-Table Local Memory** — Registry (O(1) deterministic config), Environment Variables, System Log, Memory Store.

**2. Compiler + Work Order Protocol** — L1 rule-based (zero token cost) + L2 model-based. Structured JSON Work Orders.

**3. Cloud Memory Pointer Service** — Cognitive assets co-located with inference. Imagine long-term memory living next to DeepSeek's inference cluster.

Full paper (V3.1):
- Zenodo: https://zenodo.org/records/20738982
- GitHub: https://github.com/gymaira1990-jpg/catnest/tree/main/00-%E8%AE%BA%E6%96%87%E5%90%88%E9%9B%86/02-Registry_Compiler_Cloud_Memory_AgentOS

Would love community thoughts on integrating these patterns with DeepSeek-powered agents.

Thanks!
GCat