Inspiration AI agents are becoming more powerful and they can now do much more than just answer questions. They can use different tools, access APIs, send messages and perform tasks for users. This made us think about a simple but important problem: what happens when an AI agent has access to something that it should not have? For example, an AI assistant may need access to Telegram to send notifications, but it should not automatically have access to private files, banking information or payment systems. We wanted to find a way to make AI agents useful while still keeping users in control of their access. This is what inspired us to explore how AI Passport could be used as an identity and permission layer for AI agents.
What it does AgentGuard is a concept for controlling what an AI agent can access and what actions it can perform. The main idea is that every AI agent should have an identity and a specific set of permissions. The user or organization that owns the agent decides which permissions should be given. For example, an AI agent could be allowed to search for products, compare prices, send emails or send Telegram notifications. At the same time, it would not automatically have access to banking information, payment authorization or private files. If the user no longer wants the agent to use a particular service, the permission can also be revoked. The main principle behind AgentGuard is simple: an AI agent should only be able to perform actions that its owner has explicitly allowed.
How we built it We developed AgentGuard as a concept based on the AI Passport idea. We first focused on understanding how an AI agent could have its own identity and how its permissions could be controlled. The proposed workflow is based on a user, an AI Passport, an AI agent and external tools or APIs. When the agent wants to perform an action, the system checks whether the required permission has been granted. If the permission is available, the action can continue. If the permission has not been granted or has already been revoked, the action should be denied. We also based the concept on the least-privilege principle. This means that an AI agent should receive only the access that is actually necessary for completing its task. This can reduce the possibility of an agent misusing sensitive resources.
Challenges we ran into One of the main challenges was thinking about how to make the permission system simple enough for normal users. AI agents can work with many different tools and services, so there can be many types of permissions. We wanted the system to provide enough control without making it too complicated. Another challenge was security. We had to think about possible risks such as prompt injection, stolen credentials, excessive permissions and unauthorized actions. We also considered what should happen when a user changes or revokes a permission while an AI agent is already working. These challenges made us realize that permission management is not only a technical problem. It is also about making users understand what their AI agents are allowed to do.
Accomplishments that we're proud of We are proud that we were able to turn a complex AI security problem into a simple and understandable concept. Instead of focusing only on making an AI agent more powerful, we focused on giving users more control over the agent. AgentGuard connects AI agent identity with permissions and access control. We also made permission revocation an important part of the concept because users should be able to change their decisions at any time. Another thing we are proud of is that the idea can be applied to different types of AI agents and services. The same concept could be used for communication tools, APIs, databases, files and other digital resources.
What we learned The biggest thing we learned from this project is that being capable of doing something and being authorized to do something are not the same. An AI agent may technically be capable of accessing a service, but that does not mean it should automatically have permission to use it. We believe this difference will become more important as AI agents become more autonomous and start performing more real-world tasks. We also learned more about the importance of identity, permissions, access control and security when designing AI systems. This project helped us look at AI not only from the intelligence side, but also from the trust and security side.
What's next for AgentGuard AgentGuard is currently a concept, and our next goal would be to turn it into a working prototype. We would like to connect the system with real AI agents and APIs and test how permission management works in real situations. Future versions could include OAuth and API authorization, permission expiration, real-time access revocation and detailed audit logs. We would also like to explore multi-agent environments where several AI agents work together but have different permissions. In the longer term, AgentGuard could become a permission layer for AI agents that allows people and organizations to delegate tasks without giving agents unlimited access. Our main vision is simple: AI agents should be powerful and useful, but users should always remain in control of what those agents can access and what they are allowed to do.
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