From Chatbots to Economic Agents: Autonomous Treasury Management on Base

Description and use

An autonomous agent on Base automatically rebalances a treasury portfolio using an LLM for market interpretation and strict risk limits, reducing gas costs and eliminating manual intervention.

Problem

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

Outcome

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

Workflow

• 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.

Evidence

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

Source

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

Source