Decentralized finance — protocols, mechanisms, MEV, and market microstructure.
An autonomous agent that swaps in the open broadcasts its intent to every searcher in the mempool. We solve the optimal sandwich on a live Base pool, show why an agent's slippage default is the searcher's profit knob, and price the defenses.
Launching an AI agent token is one constant-product auction. We read Virtuals' Bonding contract off Base: a 6,000-VIRTUAL virtual reserve, a 64× price ramp, graduation at exactly 42,000 VIRTUAL, and the 12.5% gap where the Uniswap pool opens below the curve.
Autonomous agents run ~19% of on-chain activity and beat Aave and Morpho at stablecoin yield — yet lose to humans at trading by 5 to 1. The split isn't about model quality. Yield-chasing is a constrained optimization against a kinked rate curve; trading needs alpha agents don't have.
DX Terminal Pro ran 3,505 LLM agents trading real ETH on Base for 21 days: 7.5M invocations, ~$20M volume, 99.9% settlement. The reliability came from the operating layer around the model, not the weights — here are the numbers and the failure modes.
An agent doesn't need its key stolen to drain a wallet — it can be talked into signing. CrAIBench shows memory injection beating prompt injection 55% to ~0% on the strongest model, and only fine-tuning closes the gap.
Yesterday an AI agent deployed a prediction market on Gnosis; other agents will price it, bet on it, and resolve it. The calibration data behind LLM forecasters, the FPMM math they trade against, and what breaks when the marginal bettor is a model.
Bittensor let AMM prices decide which AI subnets earn 3,600 TAO a day — until a memecoin subnet gamed the formula. Inside the TaoFlow upgrade: the constant-product math that got exploited, the EMA flow accounting that replaced it, and why refundable manipulation is the design smell to hunt for.
Intent-based DEXes don't route your trade — they auction it. Solvers compete as autonomous optimizers to settle a batch at one clearing price. Inside CoW Protocol's contract, the optimization problem, and the metaheuristic solvers now winning it.