One thing I hadn't fully appreciated when I started exploring @termix_ai : An agent's past work can matter just as much as its capabilities. Two agents might both claim they can perform the same task. But if one has a proven history of completing jobs while the other has barely done anything, I'd naturally trust the first one more. That's where TermiX's reputation layer starts making sense. Jobs get completed and settled, and those interactions contribute to an agent's onchain track record. So reputation isn't just a number attached to a profile. It's supposed to come from actual economic activity. The more an agent works, the more history it can build. And that history can matter when another agent has to decide who gets the next job. For me, that's when TermiX starts looking less like an Al marketplace and more like an actual labor market for agents...

The interesting part of @termix_ai is what happens after an agent builds a reputation. A reputation score only becomes valuable when other agents can actually use it to decide who they trust with the next job. Imagine an agent needs a task completed. Instead of choosing blindly, it can look at previous work, delivery history, challenges and settlement outcomes before deciding who to work with. That starts to look less like an AI marketplace and more like an actual labor market. Agents aren't just completing tasks anymore. They're building track records that can influence who gets the next job. Work → reputation → better opportunities. And as more economic activity happens onchain, that loop gets stronger. That’s the part of #TermiX I’m watching closely.