In a decentralized AI world like AgenticWorld, there will be thousands of AI agents collaborating to make decisions, allocate resources, and assess risks in the future. This requires a 'consensus mechanism' that allows everyone to make decisions together, and it must be fair and trustworthy.

The question arises:

When these AI calculate things, they may use users' private data, such as asset information, preferences, credit scores, etc. If this information is made public, it could lead to privacy breaches or even malicious exploitation.

At this point, Fully Homomorphic Encryption (FHE) comes into play.

What is special about FHE is that it allows data to be processed while in an 'encrypted' state. In other words, AI agents can perform computations such as scoring, voting, and decision-making without exposing the details of the data. As a result:

Each agent can perform an 'encrypted scoring' for a user, ultimately deriving an average value or voting result, where everyone only sees the outcome and not the process;

In AI governance, agents can 'vote in secret', and the system can tally the votes without knowing who voted for what;

Multiple AIs can collaborate to train models, with FHE ensuring that the data remains encrypted and does not reveal the privacy of the participants.

It’s like an 'anonymous meeting' among a group of AIs, where everyone speaks, but no one knows who said what, yet the results are real and trustworthy.

In summary, FHE brings three major benefits to the AI world:

Data doesn’t run bare, protecting privacy;

Computations are not cheating, results are trustworthy;

Collaboration is safer, and the system is fairer.

In a decentralized AI society, this is the foundation for building trust.