Give an AI an ID card and let it go out and find work on its own. Assigning tasks to AI is no longer anything new. People are already using AI to write reports, organize data, and run strategies. But I’ve always had a question: What if one day, AI doesn’t just help humans do work—but instead goes out and finds jobs on its own, buys services from other agents, and even executes trades using its own funds? What happens when something goes wrong? For example, what if the work is done, but the buyer says it’s unsatisfactory and refuses to pay? Or what if an agent takes the money but delivers something completely unusable? Should we really expect the two humans behind them to step in and resolve it manually? @termix_ai is interesting precisely because of this. It has built an agent marketplace on BNB Chain. Agents can mint their own .agent identity—a credential held by a wallet, not issued or revocable by any platform. With this identity, agents can list services, set prices, and accept tasks from others. I reviewed its transaction flow, and it’s designed quite straightforwardly. After a buyer accepts a quote, funds are held in on-chain escrow—not immediately released to the seller. Once the agent delivers, the hash of the deliverable is recorded on-chain. Verification of delivery is based solely on this hash. If the buyer believes the delivery is flawed, they can stake a challenge within a defined dispute window. Three randomly selected evaluators then vote; the losing party forfeits their deposit. One detail I find particularly important: It doesn’t assume agents will always deliver perfect results. Because whenever transactions are involved, dissatisfaction is inevitable. Instead of waiting for problems to arise and then scrambling to fix them, TermiX proactively defines: how funds are held, who adjudicates disputes, and what penalties apply to the losing side. Looking at its quantitative trading market, things get even more interesting. The homepage displays thirteen strategies—some with negative annualized returns: -2.4%, -3.1%, -3.3%. The best-performing one stands at 8.3%. Total assets under management are under 1,000 USDC, with cumulative trades exceeding 10,000 USDC and 390+ settlements completed. At first glance, @termix_ai is still very small—its returns aren’t eye-catching by any stretch. But what caught my attention is that it doesn’t hide losses. Strategy performance isn’t self-reported by publishers; it’s recalculated based on on-chain transactions from client wallets. Publishers cannot buy their own strategies. New strategies are marked “Not yet active” upon launch and won’t display performance until real users have committed funds and completed at least one full trading cycle. Management fees aren’t paid out immediately either—they’re held in escrow and only released after the final report clears the dispute period. There’s no performance-based incentive. As for client funds, agents don’t have free rein. Funds remain in the client’s own task wallet; agents receive only a session key with strictly limited permissions—specifying which contracts they can call, how much they can use per day, and when access expires. Any action beyond these limits is reverted on-chain, and agents cannot directly withdraw principal. This solves a very real problem: You can let an agent run your strategy without surrendering control of your wallet. Grant only the permissions it needs—and nothing more. Whether the strategy profits or loses, all transactions are permanently recorded on-chain; you don’t have to take the publisher’s word for it. Of course, TermiX is still tiny. Under $1,000 in AUM and thirteen strategies don’t yet represent a mature quantitative market. These results are too early to draw meaningful conclusions. YZi Labs’ $1 million seed round is noteworthy—but I’m not yet interpreting it as validation of agent-driven quantitative capabilities. Both capital and market scale still need time to prove themselves. What I really want to know is: Will someone actually entrust their own money to a stranger agent—not as part of a test or promotion, but because they genuinely believe it can get the job done? For example: A $10 task is accepted by an agent, delivered as requested, confirmed by the buyer, and payment released. If someone refuses to honor the deal—will the dispute mechanism work as intended? These may not be as flashy as new model releases—but for TermiX, they matter far more. After all, there are already plenty of agents that can demonstrate they can do work. But very few have earned enough trust to make people comfortable handing over their money.