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

AI agents are becoming a normal part of modern applications. Developers are building agents that can query databases, call APIs, automate workflows, interact with cloud services, and even coordinate with other agents. Getting an agent to perform a task is becoming easier. But there's another question we need to think about: Just because an AI agent can access a system, does that mean it should be allowed to perform every action? That's where things get interesting. The Problem With Giving AI Agents Too Much Access Imagine you've built an AI agent to help manage customer support requests. It needs to: Read customer information. Create and update support tickets. Retrieve order details. Send responses to customers. Everything works perfectly during testing. But what happens if that same agent can also delete customer records, modify payment information, or access unrelated internal systems? The agent may never need those permissions. Yet developers sometimes give agents broad API access because it's easier to get an integration working. This creates a security risk. An agent can behave unexpectedly, misunderstand instructions, or be manipulated through malicious content. Even when an agent is operating as designed, excessive permissions can increase the impact of mistakes. API Authentication Isn't the Same as Authorization Let's say an AI agent uses an API key to connect to your application. The API recognizes the key and accepts the request. But what does that actually prove? It proves that the request includes a valid credential. It doesn't necessarily prove that the agent is authorized to perform the specific action. For example: DELETE /api/customers/123 Authorization: Bearer A valid token should not automatically mean the agent can delete customer records. The system needs to evaluate additional information. Which agent is making the request? Who delegated authority to that agent? What actions is it permitted to perform? Is that permission still valid? These questions become especially important when agents operate autonomously. Why AI Agents Need Their Own Identities In many applications, AI agents operate using shared service accounts or credentials. This can make it difficult to distinguish between individual agents. Consider three agents working within the same organization: Agent Responsibility Required access Support Agent Customer inquiries Read customer data Finance Agent Invoice processing Read and create invoices Reporting Agent Analytics Read reporting data Each agent has a different purpose. They shouldn't all receive identical permissions. Giving agents distinct, verifiable identities creates a foundation for more precise access controls and accountability. It also makes it easier to understand which agent initiated a particular request. Delegated Permissions Matter AI agents often perform actions on behalf of someone else. For example, a user might ask an agent to generate a financial report. The agent may need temporary access to financial data to complete that task. But that doesn't mean it should receive unrestricted access to the organization's financial systems. A better approach is to delegate only the permissions required for the task. For example: { "agent_id": "reporting-agent-01", "delegated_by": "user-123", "permissions": [ "reports:read", "reports:generate" ], "expires_at": "2026-11-13T18:00:00Z" } This is an illustrative authorization-policy example, not a MudraID API payload. The idea is simple: Give agents the authority they need, for only as long as they need it. What About Agents That Use a Browser Instead of APIs? Not every AI agent interacts with systems through an API. Some agents operate through browsers, desktop applications, or existing authenticated sessions. These agents introduce another challenge. An agent using an already logged-in browser session may inherit the user's permissions without presenting a separate agent-specific credential. In that situation, API-level authorization alone may not provide enough visibility or control. Developers may need additional protections, such as isolated execution environments, restricted accounts, explicit approval for sensitive actions, and application-level authorization checks. The enforcement point matters just as much as the identity model. Where MudraID Fits In At Decryptogen, we're developing MudraID, identity and trust infrastructure designed for AI agents. MudraID focuses on three important areas: Verifiable agent identities Establishing a reliable identity for an AI agent so systems can distinguish it from other agents and services. Secure delegation of authority Helping organizations define and manage what an agent is authorized to do. Real-time verification Supporting verification of an agent's authority when it requests access to protected resources. The goal is to make AI agent identity and authorization an intentional part of application architecture rather than an afterthought. Of course, identity infrastructure is only one part of AI security. Applications still need proper authorization enforcement, monitoring, secure credential handling, and appropriate human oversight. A Question for Developers Building AI Agents As agentic systems become more common, we'll need to think more carefully about how permissions are managed. A few questions worth discussing: Are your agents using individual identities or shared credentials? Are permissions enforced at the API gateway, application layer, or somewhere else? How are you handling agents that operate through existing browser sessions? Can you revoke an agent's access without disrupting other services? I'm particularly interested in how teams are handling this in production. How are you currently managing identity and permissions for AI agents? Would love to hear what approaches are working for you. Explore MudraID: https://mudraid.ai Documentation: https://docs.mudraid.ai/overview/introduction