As AI Agents become the main narrative in the AI field, the development of the AI track is gradually entering the 2.0 era. Focusing on the concept of AI Agents, they are intelligent systems capable of perceiving the environment, making decisions, and executing tasks or services. They are usually able to understand natural language instructions, learn user preferences, and in some cases, make decisions autonomously.
AI Agents only need a given goal to think independently and take action towards that goal, breaking down the detailed steps of the given task and creating prompts for themselves based on external feedback and independent thinking to achieve the goal. We can understand AI Agent as a combination of a Q&A bot (interaction entry) + fully automated workflow (perception, thinking, action) + static knowledge base (memory).
Some typical use cases for AI Agents include autonomous driving. When a user inputs a destination, the AI Agent replaces humans to complete the driving task based on a combination of AI algorithms and various visual technologies, actively making decisions and executing actions, demonstrating true autonomy and adaptability. The gaming field is also actively experimenting with AI Agent solutions, such as using AI Agents to simulate real players, acting as opponents in games, autonomously executing tasks (like NPC behavior and storyline development), and even adjusting game difficulty based on player performance to ensure a challenging experience. In addition to these fields, many others, including manufacturing, finance, healthcare, agriculture, and cybersecurity, 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 concerns of computing power, algorithms, and data to more important issues like privacy and security.
Concerns about the Trustworthiness of AI Agents
In fact, current AI Agents are typically semi-autonomous, meaning they possess a certain degree of autonomous decision-making and task execution capabilities, but their operation still requires humans to provide clear instructions, feedback, or supervision. Generally, AI Agents can independently complete tasks or adjust behaviors within a preset range, but when faced with complex scenarios or exceeding set limits, human intervention is needed to ensure safety and accuracy.
This means that the vast majority of AI Agents largely rely on AI Prompts to achieve effective interaction between humans and AI. For readers unfamiliar with what a Prompt is, it refers to a segment of input instructions provided by the user to the AI model, guiding the AI to generate corresponding output results. A Prompt can be a question, a description, a piece of text, or even a code snippet.
For example, if I want ChatGPT to write a news article, the requirements or needs I describe to GPT in text form are a Prompt. Similarly, for a car with autonomous driving capabilities, I need to provide my destination and route preferences, which is also a Prompt.
So the question may lie precisely here.
Currently, semi-autonomous AI Agents usually rely on centralized entities. As users, they typically focus only on the Prompt and the reasoning execution results, but the process of user interaction with the AI Agent through the Prompt and the AI model's reasoning process are in a "black box," making it impossible to verify their trustworthiness.
So, during the execution process of the AI Agent, is the user's Prompt tampered with? Did the AI Agent access malicious programs while collecting information? Does the output generated by the AI Agent conform to predetermined rules or expectations, producing false or unreliable information? Is the sensitive data involved in the Prompts inputted by users (such as private keys for encrypted wallets, medical data, personal identification information, etc.) guaranteed to be private and secure? These questions may not be clearly answered.
Similarly, AI Agents overly rely on centralized servers, and their deployers and server managers hold supreme authority, which in some respects directly controls the user assets and privacy data held by the AI Agents, influencing their behavior. Some viewpoints also suggest that the current AI ecosystem is developing towards control by a few companies, which have monopolistic motives for developing and using AI models, potentially leading to a certain bias in AI models and continuously raising ethical and moral concerns.
Even some Web3-oriented AI Agents, such as AI16z's Eliza and the Virtuals protocol, only place parts of identity management, economic activities, and governance on-chain, while the core reasoning, computation, data storage, and real-time interaction and feedback of AI Agents still rely on off-chain servers, meaning the aforementioned issues still exist.
Therefore, for users, using the vast majority of AI Agent services often involves an unspoken rule of unconditional trust, with no ability to verify any part of the process. This leads to increasing skepticism about whether AI Agents are reliable, especially for use cases involving money, personal safety, etc. For example, automated on-chain transactions executed by AI Agents are often not something people dare to try.
Thus, the lack of a mechanism to verify the legality and safety of these operations in AI Agents means that until this issue is perfectly resolved, the field will remain in a "chaotic era."
Of course, the aforementioned trust concerns faced by AI Agents are not without solutions. Zypher Network has built a set of co-processing infrastructure based on zero-knowledge proofs to break through the trust dilemmas faced by the era of AI Agents.
Zypher Network: Make Agent Secure Again!
Zypher Network itself is a co-processing infrastructure centered around zero-knowledge proof solutions, capable of providing ZK services for all applications and scenarios requiring zero-knowledge proofs.
Zypher Network includes an off-chain computing network composed of distributed computing nodes and 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 miners and generate ZKP, which can be verified on the Zytron chain to ensure that data, transactions, behaviors, etc., are trustworthy and honest. The Zypher system has already been practiced in the Web3 gaming field, with dozens of Web3 games running, driven by AI (with AI Agents completing the game's logic) and capable of ensuring efficient, secure, and trustworthy operation without relying on centralized servers.
Recently, Zypher released a new zero-knowledge computing layer, providing two main core capabilities, Proof of Prompt and Proof of Inference, for the AI Agent field, proving to the public that Prompts and reasoning are correct and unaltered while not disclosing underlying sensitive data, ensuring the verifiability and trustworthiness of Prompts and reasoning during the operation of AI Agents.
It is worth mentioning that while many solutions currently attempt to bring trustworthiness to AI Agents, Zypher is the only one that does not rely on hardware and can achieve results solely through ZK cryptographic methods.
zk Prompt
As mentioned earlier, the biggest problem in traditional AI Agent models is the inability to ensure the trustworthiness of Prompts, including whether Prompts have been tampered with, whether the model reasoning is driven by accurate Prompts, and whether sensitive information in Prompts may be leaked.
Zypher is working to ensure the verifiability and trustworthiness of Prompts through the zk Prompt solution in the computing layer, aiming to guarantee the correctness and consistency of Prompts without exposing underlying data to the public or users. This is not only a key product designed for trustless AI Agents and decentralized application logic but also an important component of its trustless AI Agent development framework.
zk Prompt is presented in an easy-to-use SDK format, relying on a set of advanced cryptographic schemes, including strong encryption, Pedersen commitments, and zkSnarks (Plonk). It closely collaborates with the initialization process of system Prompts, taking the initialized Prompt as input, generating encrypted commitments through Zypher's ZK miner network, and constructing zero-knowledge proofs (ZKP).
These ZKPs allow any user or third party to verify by comparing with audited initial commitments, ensuring the correctness and consistency of Prompt content. If the actual initialization content of the system Prompt is inconsistent with the audited sample, the verification process will fail immediately, quickly locating and discovering potential issues, ensuring the transparency and reliability of system behavior.
From an operational perspective, AI Agent developers and AI Prompt application developers can use zk Prompt to create and define System Prompts, ensuring that AI models can execute specific tasks as expected.
After the System Prompt is initialized, the Prompt will be passed to the LLM model for loading, while generating an encrypted commitment through the commitment scheme, and leveraging Zypher's ZK computing network to generate immutable proofs. This process will record the integrity and consistency of the Prompt, ensuring that the Prompt can guide the model to produce expected behaviors.
For users using Prompts, they can download the committed Prompt and the corresponding proof files, and verify the current model against the committed Prompt. The verification results will clearly indicate whether the user's Prompt has been tampered with, ensuring that the Prompt and model behavior align with the developer's original settings.
Interaction Example
zk Prompt builds a reliable interaction mechanism between AI Agents, ZK computing networks, DApps, and smart contracts, ensuring the integrity and consistency of Prompts, providing trustworthy guarantees for the behavior of AI models.
When AI Agent developers define and submit System Prompts through zkPrompt, the Prompt will be encrypted, generating an encrypted commitment while initializing the AI Agent and generating encrypted circuits related to the Prompt, ensuring that the Prompt content has immutable characteristics within the system. Meanwhile, the AI Agent will send the verification key to Zypher's ZK computing network as a basis for subsequent verification.
When a DApp initiates a message or transaction request, the AI Agent will receive the request and delegate the computing task to the ZK computing network for processing. In the ZK computing network, the execution results of the Prompt are encrypted and verified through zero-knowledge proofs, which not only record the execution process of the task but also ensure the consistency between the Prompt and behavior, with the generated proof files subsequently returned to the smart contract or DApp for further verification.
Zypher's Zytron engine on-chain smart contracts will verify the zero-knowledge proofs and encrypted commitments, confirming the accuracy of the Prompt content and execution behavior. If the Prompt content has been tampered with or the execution does not conform to the original settings, the verification will fail, effectively preventing potential issues. This verification mechanism provides strong support for the reliability of Prompts and ensures that AI models can always operate according to the developer's expectations.
Thus, through collaboration with smart contracts and other blockchain objects, Zypher can achieve more transparent and verifiable security goals, conveniently integrating into many Web3 use cases.
In terms of features, zk Prompt ensures that AI Agents:
- 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.
- Trustworthiness and Transparency: Through zero-knowledge proofs, 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 of the Prompt and model through the verification process, without relying on centralized entities.
Based on zk Prompt, it not only ensures the trustworthiness of Prompts but can also extend further to Proof of Inference, ensuring that the reasoning process of AI Agents is trustworthy and that the reasoning results are generated based on legitimate inputs.
Overall, the zk Prompt solution is particularly suitable for critical task scenarios, such as those involving sensitive financial information or requiring clear action-oriented AI Agents, providing a high level of security assurance for reliability.
Enhanced Security
In the race to build trustworthy AI Agents, TEE solutions are widely adopted due to their isolated environments built through hardware, which can achieve a certain degree of data privacy protection and execution verifiability. Although TEE is a mainstream privacy solution that has been validated and widely applied in various fields, it still has certain limitations in building trustworthy AI Agents.
In fact, TEE solutions typically rely on trusted environments and key management services provided by hardware vendors such as Intel SGX and ARM TrustZone. This centralized trust mechanism makes the security of the system highly dependent on specific vendors, bringing centralized risks. Intel SGX, for example, has been exposed multiple times for vulnerabilities that directly threaten its trusted foundation. Additionally, although TEE provides an isolated runtime environment, its data privacy protection capabilities still have shortcomings. For instance, there may be eavesdropping risks during data transmission to the TEE environment, and external attackers may also obtain sensitive information through interaction interfaces. Furthermore, TEE's design is primarily aimed at predefined computing tasks, lacking dynamic adjustment capabilities. AI Agents often need to deal with changing tasks and complex contextual scenarios, making this rigid architecture difficult to meet actual needs.
In contrast, Zypher's zero-knowledge proof solution has decentralized characteristics, not relying on any centralized entities. Its security stems from a distributed and large-scale off-chain computing network cluster. This not only gives it lightweight advantages but also significantly outperforms TEE in 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 trending Deepseek and other large language models, Zypher can achieve seamless compatibility. It is worth noting that the Zypher solution is entirely based on ZK design, with pure cryptographic innovation at its core, standing out in the field of trustworthy AI Agent solutions.
Overall, although AI technology is iterating and developing at an astonishing speed, the limitations in security and ethical issues, as well as practical considerations, still pose many challenges for fully autonomous AI Agents to achieve widespread adoption. In contrast, semi-autonomous AI Agents, balancing automation and human supervision, will remain the mainstream direction for future development. This also means that AI Agents urgently need to make progress in 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 working to "Make Agent Secure Again!"