Your AI Agent Has API Access. But Who Decides What It Can Do?

AI agents are increasingly being embedded into modern software, able to interact with databases, external services and perform tasks autonomously. While this simplifies development, it raises a critical question: does access to an application's systems always equate to permission to use those systems?

The issue becomes clear when considering an AI-powered customer support agent. In development, this agent can read customer data, create tickets, pull order details and send replies. When deployed, however, granting the agent blanket access to modify payments or delete records poses significant risks. An agent could misunderstand instructions, be tricked by malicious input, or simply behave unexpectedly given excessive permissions.

The problem isn't simply one of security through API authentication - an agent merely needs a valid token to make a request. Authorization, the more granular component, determines whether that agent should indeed be able to delete a customer or modify a payment. Without verifying an agent's specific permissions, mistakes can have disastrous consequences.

To address this, agents should each have their own verified identity, distinct from shared service account credentials. This allows for precise control over what each agent can do, according to its specific role. For example, a support agent should only have read access to customer information, while a finance agent should be limited to invoice processing tasks.

Permission should also be delegated only for the duration needed to complete a task. This means an agent generating a report should only have temporary access to financial data, no longer than necessary. APIs can enforce these controlled permissions, but browser-based agents operating through existing authenticated sessions pose an additional challenge. Verification becomes more complex in these cases, requiring additional protections like isolated execution environments.

MudraID is a system designed to tackle these challenges. It provides verifiable identities for agents, tools for securely delegating authority, and real-time verification of agent permissions when accessing protected resources. While identity infrastructure alone isn't the complete solution, it's a crucial step in integrating AI agents securely into applications.

As developers integrate more agentic systems, careful consideration must be given to how permissions are managed. Questions to consider include whether agents use individual identities or shared credentials, where authorization enforcement occurs, and how to revoke access when needed. Sharing experiences and best practices on managing agent permissions in production could help organizations navigate these complexities and integrate agentic systems securely.