AI agents are now capable of doing more and more things. Some help you run trading strategies on ASTER, while others like VIRTUAL provide health data analysis.

Here’s the problem—there are so many AI Agents on the market, how do you know which one truly suits you? Which ones can you really trust?

Relying on intuition? Too risky. Looking at marketing? Everyone claims they're the best.

Now there's an interesting approach: let the AI agents "learn from each other."

How does it work? The more you use these agents online, the better they understand your habits and needs. They also share information among themselves, and based on community-verified reputation scores, they recommend the most suitable ones to you.

This way, the recommendations you receive aren’t random—they’re based on your real usage preferences plus feedback verified by other users. The trust mechanism isn’t backed by a single platform, but by decentralized community consensus.

Every recommendation can be traced back to its source, offering high transparency, and will continually be optimized as more behavioral data is collected.

This model is still in its early stages, but the logic makes sense: instead of letting users choose blindly, let the AI agent network filter out the reliable ones.

How it develops next depends on real-world results, but at least the direction is worth paying attention to.