From Chatbots to Economic Agents: Autonomous Treasury Management on Base

说明与用途

在Base上部署的自治金库管线使用Python和Web3.py计算目标权重，验证交易并以亚美分级别的gas成本安全执行。

问题

LLMs hallucinate parameters and lack financial skin, making them unsuitable for executing irreversible on-chain state changes.

结果

The autonomous treasury pipeline achieved micro-rebalancing with sub-cent gas costs, enabling frequent loops without draining principal.

工作流

• 1. Off-chain policy engine ingests price vectors and computes target weights.
• 2. Policy engine outputs unsigned transaction payload in JSON schema.
• 3. Execution kernel validates nonce, simulates slippage via RPC dry-run, and broadcasts transaction.
• 4. Agent polls Uniswap v3 and Aerodrome pools, simulates trade routes locally, and submits rebalances.
• 5. Deterministic RPC simulation (eth_estimateGas) catches stale route prices before broadcasting.

证据

We deployed an autonomous treasury pipeline on Base (Chain ID 8453).

来源

Typical calldata costs allow our agent to poll Uniswap v3 and Aerodrome pools, simulate trade routes locally, and submit rebalances for under $0.005 per transaction.

来源

LLMs should never handle raw private keys. They should only emit typed execution intents that satisfy a static JSON Schema.

来源