With the rise of AI Agent as the main narrative in the AI field, the development of the AI track has also gradually entered the 2.0 era. Focusing on the AI Agent concept itself, it is an intelligent system that can perceive the environment, make decisions, and perform tasks or services. They are usually able to understand natural language instructions, learn user preferences, and in some cases, make decisions autonomously.

The work of the AI Agent only requires a given goal, and it can independently think and take action for the goal, break down each step of the plan in detail according to the given task, and create prompts for itself through feedback from the outside world and autonomous thinking to achieve the goal. We can understand AI Agent = chatbot (interactive entry) + fully automated workflow (perception, thinking, action) + static knowledge base (memory).

Some typical AI Agent use cases include autonomous driving, where when the user enters a destination, the AI Agent will complete the driving task based on a combination of AI algorithms and various visual technologies, making decisions and executing them independently, demonstrating true autonomy and adaptability. The gaming field is also actively exploring AI Agent solutions, such as using AI Agents to simulate real players, acting as opponents in the game, autonomously executing tasks (such as NPC behavior and plot development), and even adjusting the difficulty of the game based on the player's performance to ensure a challenging experience. In addition to the above fields, production, manufacturing, finance, medical, agriculture, cybersecurity, and many other fields are also trying to apply AI Agents.

Of course, as AI Agents are widely explored in different fields, the focus of the AI field has gradually expanded from the initial computing power, algorithms, and data to more important issues such as privacy and security.

The Credibility Concerns of AI Agents

In fact, the current AI Agents are usually semi-autonomous, with certain autonomous decision-making and task execution capabilities, but their operation still requires human-provided clear instructions, feedback, or supervision. Usually, AI Agents can independently complete tasks or adjust their behavior within a preset range, but when faced with complex scenarios or beyond the set range, human intervention is required to ensure safety and accuracy.

This means that most AI Agents rely heavily on AI Prompts to achieve effective interaction between humans and AI. For readers who are not familiar with what Prompt is, Prompt refers to the input instructions or Prompt provided by the user to the AI model to guide the AI to generate the corresponding output result. Prompt can be a question, a description, a text, or even a code snippet.

For example, if I want ChatGPT to write a news article, the description of my requirements or needs that I provide to GPT in the form of text is a Prompt, or if I have a car with autonomous driving capabilities, I need to provide it with my destination and route preferences, which are also a Prompt.

So the problem may lie precisely here.

The current semi-autonomous AI Agents usually rely on centralized entities, and as users, we are usually only concerned with the Prompt and the reasoning and execution results, but the process of users interacting with AI Agents through Prompts and the process of AI model reasoning are in the "black box", and we cannot verify their credibility.

So for the user's Prompt, is it tampered with during the execution process of the AI Agent? Did the AI Agent access any malicious programs while collecting information? Does the output generated by the AI Agent comply with the predetermined rules or expectations, producing false or unreliable information? Are the sensitive data (private keys of encrypted wallets, medical data, personal identity information, etc.) involved in the Prompt input by the user to the AI model guaranteed to be private and secure? These may not be very clear.

Similarly, AI Agents are overly dependent on centralized servers, and their deployers and server administrators have the highest authority, to some extent controlling the user assets and privacy data held by the AI agents, and influencing the behavior of the AI agents. There are also some views that the current AI ecosystem is developing towards the control of a few companies, and these upstream companies have the motivation to monopolize the development and use of AI models, which may lead to certain biases in the AI models and continue to raise concerns in terms of ethics and morals.

Even for some Web3-oriented AI Agents such as Eliza from a16z and the Virtuals protocol, they only put identity management, economic activities, and governance on the chain, while the core reasoning and calculation, data storage, and real-time interaction and feedback of the AI Agent still depend on off-chain servers, and the above problems still exist in essence.

So for users, when using most AI Agent services, the implicit rule is to unconditionally trust them, and yet they cannot verify the credibility of any link. This has led to more and more people being skeptical about whether AI Agents are reliable, at least for some use cases involving money and personal safety, such as automated on-chain transactions executed by AI Agents, which they are usually reluctant to try.

So for the AI Agent itself, there is no mechanism to verify the legality and safety of these operations, and until this problem is perfectly solved, the field will always be in a "chaotic era".

Of course, the credibility concerns faced by AI Agents are not unsolvable, and Zypher Network has built a set of co-processing infrastructure based on zero-knowledge proofs to break the credibility dilemma faced by the AI Agent era.

Zypher Network: Make Agent Secure Again!

Zypher Network itself is a co-processing infrastructure with zero-knowledge proof schemes as its core, which can provide ZK services for all application scenarios and facilities with zero-knowledge proof requirements.

Zypher Network itself contains an off-chain computing network composed of distributed computing nodes, as well as an on-chain engine called Zytron. When there are zero-knowledge computing tasks in the Zypher network, the Zypher system will assign computing tasks to the miners and generate ZKPs, which can be verified on the Zytron chain to ensure that the data, transactions, and behaviors are credible and honest. Zypher system has already been practiced in the Web3 gaming field, and dozens of Web3 games are running, which are driven by AI (completed by AI Agents) and can run efficiently, securely and reliably without relying on centralized servers.

Recently, Zypher has released a new zero-knowledge computing layer, which provides two core capabilities, Proof of Prompt and Proof of Inference, for the AI Agent field. By publicly proving that the Prompt and reasoning are correct and unaltered, while not revealing the underlying sensitive data, it ensures the verifiability and credibility of the Prompt and reasoning in the AI Agent's operation process.

It is worth mentioning that although there are many solutions trying to bring credibility to AI Agents, Zypher is the only one that can achieve this effect without relying on hardware, but only through ZK cryptographic means.

Zk Prompt

As mentioned earlier, the biggest problem in the traditional AI Agent model is the inability to ensure the credibility of the Prompt, including whether the Prompt has been tampered with, whether it is driven by the accurate Prompt to model the reasoning, and whether the sensitive information in the Prompt will be leaked.

Zypher is using the zk Prompt solution in the computing layer to ensure the verifiability and credibility of the Prompt, aiming to guarantee the correctness and consistency of the Prompt, while not exposing the underlying data to the public or users. This is not only a key product for building trustless AI Agents and decentralized application logic, but also an important component of its trustless AI Agent development framework.

zk Prompt itself is presented in the form of an easy-to-use SDK, relying on a set of advanced encryption schemes, including strong encryption, Pedersen commitment, and zkSnarks (Plonk) primitives. It closely collaborates with the system Prompt initialization process, taking the initialized Prompt as input, generating encrypted commitments through Zypher's ZK miner network, and constructing zero-knowledge proofs (ZKPs).

These ZKPs allow any user or third party to verify, by comparing with the audited initial commitment, the correctness and consistency of the Prompt content. If the actual initial content of the system Prompt is inconsistent with the audit sample, the verification process will fail immediately, quickly locating and discovering potential problems, ensuring the transparency and reliability of the system behavior.

From the perspective of the development process, AI Agent developers and AI Prompt application developers can use zk Prompt to create and define System Prompt, ensuring that the AI model can perform specific tasks as expected.

After the initialization of the System Prompt, the Prompt will be passed to the LLM model for loading, and a commitment will be generated through the commitment scheme, and an irrefutable proof will be generated with the help of Zypher's ZK computation network. This process will record the integrity and consistency of the Prompt, ensuring that the Prompt can guide the model to produce the expected behavior.

For users who use Prompt, they can download the committed Prompt and the corresponding proof file, and verify the current model with the committed Prompt. The verification result will clearly indicate whether the user's Prompt has been tampered with, thereby ensuring that the Prompt and the model's behavior are consistent with the original settings of the developer.

Interaction Example

zk Prompt has built a reliable interaction mechanism between AI Agent, ZK computation network, DApp, and smart contracts, ensuring the integrity and consistency of Prompt, and providing a trustworthy guarantee for the behavior of AI models.

After the AI Agent developer defines and submits the System Prompt through zkPrompt, the Prompt will be encrypted and a commitment will be generated, while initializing the AI Agent and generating the encrypted circuit related to the Prompt, ensuring that the Prompt content has the characteristic of being tamper-proof in the system. At the same time, the AI Agent will send the verification key to Zypher's ZK computation network as the basis for subsequent verification.

When the DApp initiates a message or transaction request, the AI Agent will receive the request and delegate the computation task to the ZK computation network for processing. In the ZK computation network, the execution result of the Prompt is verified in the form of a zero-knowledge proof, which not only records the execution process of the task, but also guarantees the consistency between the Prompt and the behavior, and the generated proof file is then returned to the smart contract or DApp for further verification.

The on-chain smart contract of Zypher's Zytron engine will verify the zero-knowledge proof and the encrypted commitment, confirming the accuracy of the Prompt content and the execution behavior. If the Prompt content is tampered with or the execution does not match the original setting, the verification will fail, effectively preventing potential problems. This verification mechanism provides strong support for the reliability of Prompt and ensures that the AI model can always run according to the developer's expectations.

Therefore, by collaborating with smart contracts and other blockchain objects, Zypher can achieve more transparent and verifiable security goals, and can be conveniently integrated into various Web3 use cases.

In terms of features, zk Prompt can ensure that the AI Agent:

• Data Privacy: Users can verify the correctness of the Prompt without seeing or understanding the specific content of the system Prompt, protecting the sensitivity of the Prompt.
• Credibility and Transparency: Through zero-knowledge proof, users can trust that the AI's behavior has not been maliciously tampered with.
• Distributed Verification: Any user or third party can confirm the consistency between the Prompt and the model through the verification process, without relying on a centralized entity.

Based on zk Prompt, it not only can guarantee the credibility of the Prompt, but also can further extend to Proof of Inference, which can also ensure that the reasoning process of the AI Agent is trustworthy and the reasoning result is generated based on legitimate input.

Overall, the zk Prompt solution is particularly suitable for critical task scenarios, such as those involving financial sensitive information or requiring clear action guidance for AI Agents, to ensure reliability and provide a high level of security guarantee.

Better Security

In the race to build trustworthy AI Agents, the TEE solution has been widely adopted due to its isolated environment built on hardware, which can achieve a certain degree of data privacy protection and verifiable execution. Although TEE is a mainstream privacy solution that has been verified and widely used in many fields, it still has certain limitations in building trustworthy AI Agents.

In fact, the TEE solution usually relies on the trusted environment and key management services provided by hardware vendors such as Intel SGX and ARM TrustZone. This centralized trust mechanism makes the system's security highly dependent on specific vendors, bringing centralized risks, and Intel SGX has been exposed to vulnerabilities multiple times, directly threatening its trusted foundation. In addition, although TEE provides an isolated runtime environment, its data privacy protection capability is still insufficient. For example, there may be eavesdropping risks during the data transmission to the TEE environment, and external attackers can also obtain sensitive information through the interaction interface. Furthermore, the design of TEE is mainly aimed at predefined computing tasks, lacking the ability to dynamically adjust, while AI Agents often need to deal with changing tasks and complex contextual scenarios, and this rigid architecture is also difficult to meet actual needs.

In comparison, Zypher's zero-knowledge proof solution has a decentralized nature, without relying on any centralized entity, and its security comes from the distributed and large-scale computation network cluster in the off-chain. This not only gives it a lightweight advantage, but also is clearly superior to TEE in terms of scalability and dynamic flexibility, allowing it to adapt more efficiently to the diverse application scenarios of AI Agents. Whether it's ChatGPT or the currently hot Deepseek and other large language models, Zypher can achieve seamless compatibility. It is worth mentioning that the Zypher solution is based entirely on ZK design, with pure cryptographic innovation as its core, and it stands out in the field of trustworthy AI Agent solutions.

Overall, although AI technology is iterating and developing at an amazing speed, due to the limitations in terms of security and ethics issues, as well as practical considerations, the full-autonomous AI Agent still faces many challenges before achieving widespread adoption. In comparison, the semi-autonomous AI Agent, which balances automation and human supervision, will still be the mainstream direction of future development. This also means that AI Agents urgently need progress in terms of trustworthiness and privacy before large-scale adoption, and Zypher Network, with its fully ZK-based cryptographic solution, is accelerating this process and providing a solid foundation for the next stage of development in the AI Agent track.

As the most important cryptographic infrastructure in the AI era, Zypher Network is "Making Agent Secure Again"!