From Chatbots to Economic Agents: Autonomous Treasury Management on Base

说明与用途

Base 上的自治代理使用 LLM 进行市场解读并执行严格的风险限制，自动重新平衡财务组合，降低交易费用并消除人工干预。

问题

Automating treasury management on Ethereum mainnet was costly and risky due to high gas fees and reliance on centralized custody or complex multisigs.

结果

The agent achieves near‑real‑time treasury rebalancing on Base with minimal gas costs, while maintaining deterministic risk controls and eliminating manual intervention.

工作流

• 1. Ingest blockchain state, mempool events, and protocol yields via RPC.
• 2. Run deterministic risk engine with hard‑coded circuit breakers and an LLM for market interpretation.
• 3. Emit structured JSON intent for transaction.
• 4. Validate intent locally (slippage, allowances, target addresses).
• 5. Build, sign, and send transaction using web3.py and the agent’s private key.
• 6. Monitor execution and enforce hard outflow ceilings per epoch.

证据

On Chain ID 8453, sub‑cent transaction fees and predictable sequencer finality allow AI agents to run tight control loops.

来源

The most critical architectural rule: Never let an LLM write raw transactions directly to the RPC.

来源