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We’re testing an AI agent that needs to call a few third-party services, which means it somehow needs access to API credentials. Giving the model the actual tokens feels like a bad idea, especially when it can also read emails, documents, or other untrusted content that might contain malicious instructions. How are people letting agents authenticate with external services without exposing the credentials to the model itself? Is there a practical way to keep the secrets separate while still allowing the agent to make the calls it needs?
Put a trusted gateway between the agent and each external API. The model calls a named tool with structured arguments, and the gateway pulls the right credential from a vault, adds it to the request, and sends back the result. Give each tool a token that can access only the endpoints it actually needs, and deny requests to every other destination. A malicious email can still tell the agent to do something stupid, but it can’t make the model reveal a token it never receives.
That setup gets more complicated as you add tools, because credential isolation is only one part of the problem. You also need to control where the agent can send data and keep a record of every approved or blocked call. For secure AI agents, you can use this platform: https://agyn.io/ . It keeps credentials outside the model and injects them only into approved outbound requests. Each agent also gets separate permissions and egress rules, while tracing shows every action taken during a run.
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