In April 2025, Dr. George, co-founder of Mind Network, was invited to participate in a live AMA hosted by Binance, titled "Binance Chinese Influencers Face-to-Face AMA," where he conducted an in-depth sharing on "How FHE Constructs a Trustworthy and Secure Infrastructure for Agentic AI."
George systematically explained the vision and technological path of Mind Network, shared the core issues that need to be addressed in the integration of AI and Web3, and how Mind Network provides answers through FHE technology.
Below is a summary and key points of the Q&A content.
1. The Future Form of Agentic AI and Mind Network's Positioning
Dr. George pointed out that the evolution of AI technology can be divided into five stages. The large models we are familiar with today, such as ChatGPT, Gemini, and DeepSeek, mainly belong to the first two stages:
- L1: Conversational AI: Understanding natural language and interacting;
- L2: Reasoning AI: Capable of simple logical judgments and planning;
- L3: Task-oriented Agent: An automated tool that completes tasks according to set processes;
- L4: Autonomous Agentic AI: Capable of independent thinking, judgment, planning, and executing complex tasks;
- L5: Organizational AI: Multiple intelligent Agents working collaboratively to form a self-organizing system, known as the "Agent World."
Mind Network focuses on the critical leap from L3 to L4, aiming to create truly autonomous and collaborative Agent AI.
George proposed that Agent AI will become the core form of deep integration between Web3 and AI in the next 2–10 years, and the key foundation for achieving this goal is the popularization and implementation of FHE technology.
2. FHE: The Core Technology Connecting AI and Web3
Dr. George pointed out that current blockchain systems and ZK (Zero-Knowledge Proof) technology face three essential bottlenecks when serving Agentic AI:
- Verification Issue: How to trust the execution results of off-chain AI on-chain?
- Consensus Issue: How can multiple AI Agents reliably collaborate and reach decisions?
- Encryption Issue: How to perform computations and interactions under the premise of data encryption?
The solution proposed by Mind Network is to use FHE technology as the underlying execution environment, achieving a running mechanism of "data always encrypted, processes verifiable, results consensus-based." By constructing FHE VM, on-chain verification layers, and consensus protocols, Mind Network encapsulates these capabilities into development modules and SDKs for developers to build trustworthy Agents.
This model has been successfully applied in various scenarios, such as verifying the integrity of DeepSeek model versions or allowing multiple quantitative trading Agents to reach consensus decisions without disclosing their strategies.
Additionally, Mind Network's core platform, AgenticWorld, has officially launched, supporting developers to train and deploy Agents through FHE and explore multi-Agent protocol standards such as MCP and A2A.
3. Four Pillars of Security: The Technical Foundation of a Trustworthy Agent World
To support the trustworthy operation of Agentic AI in the real world, Mind Network has established four core security pillars:
- Data Security: Achieving encryption protection of user data during computation through FHE;
- Communication Security: Ensuring that interactions between Agents use encrypted channels to prevent data leakage and identity spoofing;
- Computation Security: Ensuring that AI reasoning and execution logic are encrypted and verifiable throughout the process;
- Consensus Security: Allowing multiple Agents to form trustworthy consensus on the same task, addressing the risk of probabilistic errors in AI.
FHE serves as the underlying mathematical foundation supporting these four security capabilities, breaking the old paradigm of "computation must be decrypted," making true privacy computing possible.
For example, in the integration of DeepSeek, users can verify whether the DeepSeek model they are using is the officially released version and interact with the model while maintaining the confidentiality of input data. This mechanism significantly enhances the usability of large models in scenarios with high privacy requirements, such as enterprises and finance.
4. Multi-Chain Support: A Unified Operating Environment for Cross-Chain Agents
From the outset, Mind Network has supported multi-chain deployment, including the first FHE chain, MindChain, and the mainstream public chain, BNB Chain (BSC).
This decision stems from the understanding that "the popularity of Agent AI = user experience": Agents trained on one chain should have the ability to participate in work across chains, avoiding "capability fragmentation" and "experience disconnection."
Mind Network has built a cross-chain synchronization mechanism that supports Agent states, tasks, and incentives, which is not a traditional bridging protocol but a foundational architecture for cross-chain collaboration serving the Agent lifecycle.
Users can deploy Agents on BSC and participate in tasks on MindChain, and vice versa, with their capabilities and rewards remaining consistent across the multi-chain environment. This mechanism also lays the foundation for the future global deployment of AgenticWorld.
5. $FHE: The Economic Engine Driving AgenticWorld
At the economic model level, Dr. George introduced the native utility token $FHE launched by Mind Network, which can play three major roles in AgenticWorld:
- Service Exchange Medium: Agents must pay with $FHE to call services and purchase model capabilities;
- Ecosystem Incentive Tool: Developers and users can earn $FHE rewards by participating in training, deployment, and collaboration;
- Community Governance Credential: Used for on-chain governance, Agent whitelisting, parameter upgrades, and other voting scenarios.
Currently, $FHE has been launched on Binance Launchpool and mainstream trading platforms, with specific token distribution and economic mechanisms detailed in the official documentation, aimed at ensuring the long-term sustainable development of the community.
At the end of the AMA, Dr. George responded to several questions raised by community users. Below is the free Q&A section:
1. Is FHE technology really useful in real scenarios where there is "no mutual trust"? Can you provide examples?
Yes. Mind Network has deployed an FHE-based random number generation mechanism on-chain: multiple Agents provide encrypted random inputs, which are aggregated to generate the final random number. No participating party can tamper with or predict the final result.
This method is suitable for collaborative scenarios that require privacy protection and lack a trust foundation, such as jointly generating seeds, submitting model validations, and consensus decisions.
2. Does using FHE make it possible for AI to be treated as a "black box" tool? Is there a risk of privacy leakage or misuse?
FHE itself is a neutral technology, and its core value lies in ensuring security and privacy during the encrypted computation process. The Agents built by Mind Network are aimed at compliant and trustworthy use cases, such as model validation, multi-Agent consensus, and secure computation of Web3 assets.
Regarding the issue of "reverse engineering input data," George pointed out that FHE uses quantum-resistant encryption algorithms, which mathematically cannot be reverse-engineered unless there is a bug in the system itself. Overall, the technology itself is secure and trustworthy.
3. Is Mind Network's FHE self-developed? Will it be very "heavy" or affect usage efficiency?
FHE indeed has a high computational cost, which is determined by its mathematical characteristics. Mind Network adopts a " off-chain execution + on-chain verification" architectural design, performing ciphertext computations off-chain through FHE VM and only uploading necessary results on-chain, maximizing the balance between privacy and efficiency.
At the same time, Mind Network maintains close cooperation with open-source cryptographic companies like Zama on the underlying FHE technology, continuously optimizing operational efficiency and module compatibility.
4. Is AgenticWorld online? Can ordinary users participate?
Yes, AgenticWorld is online, currently supporting the creation and registration of personal intelligent Agents; users can participate in Hub tasks on-chain and earn rewards. The first Advance Hub and DeepSeek Hub went live yesterday.
Additionally, developers can earn $FHE incentives by participating in testing, submitting PRs, and validating models.
5. Are Agents only suitable for developers? Will there be tools aimed at ordinary users in the future?
Agents do not only serve developers. In the future, there will be a large number of "C-end user-oriented" Agent tools launched, including but not limited to:
- Web3 asset monitoring Agents;
- Automated research/trading assistants;
- Daily task execution tools for multi-Agent collaboration.
Mind Network is building an ecosystem similar to an Agent App Store, allowing ordinary users to manage and call their on-chain Agents just like using apps, enabling Agents to work for users and generate income.
Conclusion
Mind Network is building a trustworthy and secure operational paradigm for the next generation of AI. Based on FHE, through data encryption, communication encryption, computation verification, and consensus mechanisms, Mind Network has created a modular infrastructure suitable for Agentic AI.
By supporting multi-chain operations, providing token incentives, and opening SDKs and development environments, Mind Network is accelerating the implementation of this infrastructure among global developers and intelligent systems.
This is not only a positive response to the integration of AI and blockchain technology but also lays the foundation of trust and collaboration for the future AgenticWorld.
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