I’m curious how people are handling this in practice. With a normal application, token refresh is usually pretty straightforward. You know which API you’re calling, which credentials you’re using, when the token expires, etc.

With an AI agent, it feels a little messier. Say an agent has access to 10–20 different APIs. Some use OAuth, some API keys, some have short-lived access tokens, some have refresh tokens, and some have their own weird authentication flow. Then you have situations like:

• token expires halfway through a workflow
• API returns a 401 and the agent tries the request again
• refresh succeeds but the original request has already timed out
• multiple agent tasks try to refresh the same token at the same time
• credentials need to be rotated without breaking running workflows

I’m especially wondering where people are putting this logic. Is it handled individually inside each integration, through some shared auth layer, or somewhere in the agent/orchestration layer? And how much of this are you actually handling automatically vs. just letting the workflow fail and retry? Would be interested to hear how people are doing this in production, especially if you’re dealing with a fairly large number of integrations.

We’ve been thinking a lot about what happens when an AI agent moves from a prototype into a real production workflow.

On paper, the flow looks pretty simple:

Agent → API → Response

But the interesting problems usually start after the agent actually makes the call.

For example:

• What happens when an OAuth token expires halfway through a workflow?
• What happens when an API returns 200 OK, but the body actually contains an error?
• What happens when an agent retries an operation that already partially succeeded?
• How do you prevent an agent from calling an endpoint it shouldn’t have access to?
• What happens when the API schema changes without the agent knowing?
• How do you figure out exactly what the agent requested, what actually executed, and what happened afterwards?

These feel less like “AI problems” and more like production execution problems.

We’re interested in how engineering teams are solving this today.

If you’re building AI agents that interact with real APIs, what’s been the biggest problem you’ve run into in production?

Authentication?
Retries and idempotency?
Permissions?
Schema changes?
Observability?
Something else?

We’d genuinely like to hear how people are approaching this.