AI agents are starting to do more than answer questions.
They can call APIs, update databases, retrieve customer information, and trigger workflows without someone manually approving every step.
That's useful, but it also raises a question:
How do we make sure an AI agent only does what it's allowed to do?
The Problem With Giving Agents API Keys
Let's say you've built an AI agent that helps your support team.
It needs to read customer records, check order details, and occasionally update a support ticket.
The easiest approach is to give it an API key with access to those systems.
But what happens if that key also allows the agent to delete customer records or modify payment information?
The agent might never need those permissions, yet it still has them.
And things become more complicated when one agent starts calling another.
What Happens When Agents Work Together?
Imagine a simple workflow:
Agent A receives a customer request.
Agent A asks Agent B to retrieve information.
Agent B calls an internal API.
The API returns sensitive customer data.
Now consider a few questions.
How does the API know which agent made the request?
How do we know Agent B was actually authorized by Agent A?
And what happens if Agent B tries to access something outside the original task?
These are problems that traditional API authentication doesn't always solve on its own.
A Better Way to Handle Agent Access
There are a few approaches worth considering.
- Give each agent its own identity
Instead of sharing credentials, each agent should have an identity that can be verified.
- Limit permissions to the task
An agent that only needs to read customer information shouldn't receive permission to modify or delete it.
- Verify access when requests happen
Authentication alone isn't enough. The system should also check whether the agent is authorized to perform the specific action.
- Track agent activity
When something goes wrong, developers need to know which agent performed an action and under what authority.
Where MudraID Fits In
We've been exploring these challenges while working on MudraID at Decryptogen.
MudraID focuses on identity and access management for AI agents, including verifiable agent identities, secure delegation of authority, and real-time access verification.
The goal is to help developers manage agent permissions without relying entirely on shared credentials or scattered access rules.
You can learn more at https://mudraid.ai/.
What Are Developers Doing Today?
I'm curious how other teams are approaching this.
Are you using OAuth scopes, short-lived tokens, API gateways, or custom permission checks?
And if you're running multiple agents in production, how are you handling permissions when one agent delegates work to another?
Would be interested to hear what's working and what has been difficult to implement.
