Chunk 32.1

The chunk begins with a deep-dive into the production crash, where the assistant confirmed the user's theory that vast.ai enforces a separate `mem_limit` via a host-side watchdog, distinct from cgroups, explaining the silent process terminations. This discovery catalyzed a strategic pivot from reactive debugging to proactive automation: the user directed the assistant to build a fully autonomous agent to manage the fleet, scale it based on Curio SNARK demand, and alert humans when necessary. The assistant rapidly executed this vision, first researching SOTA agent APIs and assessing the `qwen3.5-122b` model (which passed all tool-calling tests). A comprehensive Go API was built for the vast-manager (`agent_api.go`), exposing 12 endpoints for demand monitoring, fleet status, instance lifecycle (launch/stop with safety guards), alerting, and per-machine performance tracking. A Python autonomous agent (`vast_agent.py`) was created to run on a 5-minute systemd timer, using the LLM to make scaling decisions. The system was deployed to the management host, and the agent autonomously launched its first instance. The user immediately provided critical operational feedback that reshaped the agent's logic: pending task counts are highly volatile and a poor signal for a system where instance startup takes hours. The agent was redesigned around simple, robust rules—scale up to a target proofs-per-hour capacity when demand is active, scale down after an hour of inactivity, and prefer historically proven machines via a performance markdown file. The final iteration successfully demonstrated the agent observing fleet state, understanding loading instances, respecting rate limits, and making sound decisions, establishing a solid foundation for autonomous, cost-effective cluster management.

From Silent Crashes to Autonomous Operations: Building an LLM-Driven Fleet Management Agent for GPU Proving Infrastructure 1921 words

Message Articles

Subagent Sessions