From Chatbots to Economic Agents: Autonomous Treasury Management on Base

Description and use

An autonomous treasury pipeline on Base uses Python and Web3.py to calculate target weights, validate transactions, and execute them safely with sub-cent gas costs.

Problem

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

Outcome

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

Workflow

• 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.

Evidence

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

Source

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.

Source

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

Source