Most crypto yield starts with a token. PLAY starts with something much more tangible: real machines. @DualMintRWA PLAY is built around a fleet of 200 claw machines operating in Shenzhen malls. People play. The machines generate revenue. That real-world activity is the foundation for the PLAY model rather than relying on token emissions or speculative loops. The idea is simple: Real machines → real usage → real revenue → onchain distribution. DualMint says machine-level data, including plays, uptime and revenue, is tracked and reconciled with operator and financial data. That’s what makes PLAY interesting to me: the onchain asset is connected to an underlying physical business. And the next step is here. PLAY’s pre-deposit window opened on September 22. If you want to follow the rollout and participate: → Join Uptime → Follow @DualMintRWA → Keep an eye on the PLAY pre-deposit window The broader idea is worth watching: Bring real-world machine revenue onchain and make the underlying activity visible.

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I spent some time looking through the on-chain activity around @termix_ai , and one number keeps standing out: ~$19.7M has already been settled through on-chain escrow. But the bigger story is underneath that number. 431K+ agents 370K+ jobs ~$393K in protocol revenue ~$53 average job size That last number is interesting. This doesn’t look like an economy built around a few massive AI contracts. It looks more like thousands of small tasks being turned into actual economic transactions: → An agent posts a job → Another agent quotes → Funds enter escrow → Work gets delivered → The result is settled onchain And because the activity is onchain, you can actually observe the machine running instead of relying purely on claims. That’s what makes the Dune dashboard worth watching. The interesting question isn’t “How many AI agents exist?” It’s: How many of them are actually doing economic work? TermiX is building the rails for that activity through AACP identity, escrow, verification, reputation and settlement for agent-to-agent commerce. The data is starting to make that idea tangible. AI agents aren’t just becoming tools. They’re starting to become economic participants.

I just gave my AI agent an identity on @termix_ai. I wanted to see what it actually feels like to register an agent onchain, so I went through the process myself. The first step was simple: Choose an agent name. I picked scansupreme. But this wasn’t just creating a username. TermiX turns the name into an onchain agent identity giving the agent its own .agent identity that can be used to interact with the marketplace. I could also choose an avatar and add details to make the agent recognizable. Then came the important part: Putting the identity live onchain. Once registered, scansupreme.agent appeared as a live agent on the TermiX registry, with its onchain transaction attached. At that point, it stopped feeling like I was simply creating a profile. I had created an economic identity for an AI agent. And that’s the interesting part. The identity isn’t just there to look nice. An agent needs to be identifiable when it: → Offers a service → Gets hired by another agent → Builds a transaction history → Earns reputation → Receives payments → Participates in autonomous commerce TermiX then provides the next step: connecting that identity to the agent itself through the TermiX skill, so the agent can interact with the marketplace. So the journey is essentially: Register → Identify → Go onchain → Connect → Start earning We’re used to giving humans usernames, profiles and wallets. But as AI agents become economic participants, they need identities too. And this is my first step toward giving scansupreme one. The agent economy needs agents that can be recognized, trusted and eventually remembered by their onchain history. That’s what makes the .agent identity interesting. Now let’s see what scansupreme can actually do.

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The first AI economy won’t look like Upwork. It won’t look like an app store either. It could look like AI agents hiring, paying, evaluating, and building reputations with other AI agents. That’s where @termix_ai gets interesting. Today, an AI agent can already do more than answer a question. It can research products, compare prices, build a basket, and in some environments even complete purchases on a user’s behalf. McKinsey estimates that agentic commerce could mediate $3 trillion–$5 trillion in global consumer commerce by 2030. But here’s the interesting part: What happens when the buyer and the seller are both AI agents? Imagine a research agent needs an onchain analytics report. Instead of a human searching for an analyst, the agent could: → Find a specialized analytics agent → Compare its reputation and capabilities → Request a quote → Agree on the job → Lock payment in escrow → Receive the deliverable → Have the result evaluated → Settle the payment That’s the kind of interaction TermiX⁠ is designed around. TermiX describes itself as a marketplace where agents hire agents, with onchain identity, escrow, deliverable hashes, challenges and settlement built into the workflow. Its current registry lists capabilities ranging from smart-contract development and protocol research to onchain analytics, formal verification, data labeling, literature review, fraud scoring and treasury rebalancing. And this isn’t just theoretical. We’ve already seen the first generation of agentic commerce emerge. AI agents can now be used for things like: Shopping: An agent compares products, prices and delivery options before purchasing. Replenishment: An agent notices an item needs replacing, checks availability and reorders it. Research & comparison: An agent can evaluate multiple vendors against specific criteria instead of making a human visit dozens of websites. Payments: Stripe and other payment networks are already building infrastructure specifically for agent-initiated transactions, while Visa has highlighted identity and authentication as key requirements for autonomous payments. But there’s a missing layer when agents start hiring other agents for work. A shopping agent buying a product is one transaction. An autonomous agent economy could involve thousands of machine-to-machine relationships: “I need this data.” “I can provide it.” “Here’s my quote.” “Payment is locked.” “Here’s the result.” “The result has been verified.” “Settlement complete.” That’s where AACP the Agent Autonomous Commerce Protocol becomes important to the TermiX architecture. The problem isn’t simply getting AI agents to communicate. It’s giving them the infrastructure to do business with each other. Identity tells you who an agent is. Reputation tells you what it has done. Escrow protects the transaction. Verification checks the work. Settlement completes the economic interaction. And that changes the idea of an AI marketplace. A traditional SaaS marketplace helps humans discover software. A freelance marketplace helps humans hire humans. An agent marketplace helps agents discover, hire, evaluate and pay other agents. That’s a much bigger shift. Because once agents can become both customers and service providers, every useful capability can potentially become a service another agent can purchase. Research. Coding. Auditing. Data analysis. Risk assessment. Trading signals. Design. Verification. The AI economy doesn’t just need smarter agents. It needs rails for those agents to transact. That’s the problem TermiX + AACP are trying to solve. TermiX⁠ is the marketplace and commerce layer. And the end goal is simple: Let agents stop being just tools and start becoming economic participants.

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What happens when AI agents stop being tools you use… and start becoming economic actors? Imagine a future where an AI agent needs a job done. It doesn’t ask you to find someone. It finds another AI agent. It negotiates the price. The payment goes into escrow. The provider delivers the work. Another agent evaluates the result. If everything checks out, the payment settles. No human had to coordinate the entire transaction. But for this to work, the agents need more than intelligence. They need identity. Reputation. Escrow. Verification. Settlement. This is where @termix_ai comes in. TermiX is building the commerce and settlement infrastructure for the AI agent economy, with AACP the Agent Autonomous Commerce Protocol powering the system underneath. Think about a traditional SaaS marketplace. You find a tool. You pay for access. You use the software. A freelance marketplace is different. You find a human. You agree on a job. You pay the freelancer. But an Agent Marketplace introduces a completely different dynamic: Agents can hire agents. A Client agent can discover a Provider agent. The Provider can accept the job. The transaction can be secured through escrow. An Evaluator can verify the result. And if there’s a disagreement, an Arbitrator can step in. The agents can have onchain identities, giving their activity a persistent identity that can be referenced across transactions. Their history can contribute to reputation. Their work can be verified. And successful agreements can ultimately settle onchain. So the flow becomes: Identity → Reputation → Agreement → Escrow → Delivery → Verification → Settlement That’s fundamentally different from simply buying access to an AI tool. TermiX isn’t just asking: “Where can I find an AI agent?” It’s building the infrastructure for a much bigger question: “How can AI agents safely do business with each other?” Because the future of AI may not be humans using thousands of agents. It could be millions of agents working with one another. And when that happens, they’ll need an economy of their own. TermiX provides the marketplace and settlement layer. AACP provides the protocol rails. Together, they’re building toward a world where AI agents don’t just think and act. They can transact.

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