Can an office-grade AI assistant run a self-governing system on your cloud server? In this guide we share how the Huodou Cloud AI AIOS architecture was built: the "brain" lives in an office AI session (planning, memory, decisions), while the "hands" are lightweight scripts on the server (execution, patrol, archive). Result: daily human intervention dropped to nearly zero.
Key insight: large models are great at deciding, bad at staying resident. We split the system into two layers connected by a memory gateway protocol — every node reports structured "truths" to a shared truth DB with per-node DID identity and full audit trail.
# SSH key + core services
ssh-keygen -t ed25519 && ssh-copy-id user@server
python3 gateway_server.py & # memory gateway :18080
python3 truth_db.py & # truth database (SQLite)
# cron: heartbeat every 5 min
# Memory gateway: single-header auth
curl -X POST https://your.domain/api/report/truth \
-H "X-DID: DID-xxxx" -H "Content-Type: application/json" \
-d '{"key":"heartbeat.node1","value":{"status":"OK"}}'Patrol: scheduled scans of processes, disk, endpoints — anomalies auto-logged. Self-heal: dead wrapper auto-restarted by cron. Archive: after any change, local snapshot (SHA-256) + cloud archive + truth report, forming an append-only audit chain.
One month of operation: zero manual incident handling; ~2 vCPU / 4GB cloud footprint; orchestration cost mostly office-AI quota — far cheaper than resident LLM inference. Works for personal clouds, small-team automation, and AIGC pipelines.
1) Write meta-rules first (what auto-runs vs. what needs human approval) before code. 2) Truth sovereignty: every node has a DID; all writes auditable. 3) Fault-tolerant by design: failures never block the main path. Full blueprint: Cloud Deployment Engine whitepaper and asset portal.