When AI Runs a Real Cost-Approval Process – Restoring Files from Cold Storage

Built with Mobileye’s cloud support team

The Problem

To control storage spend, organizations move infrequently accessed data to cold tiers like S3 Glacier and Deep Archive. The complication comes when users need it back: restoring large volumes can cost thousands of dollars in a single operation, so systems block the action until someone authorizes it. Users hit a permission error listing object count, size, and estimated cost – and open a ticket.

The balance is delicate: avoid blanket approvals that inflate cloud costs, without delaying legitimate research.

The Solution

The restore-approval agent manages the workflow end to end:

• If the user already ran the restore and pasted the error (objects, size, estimated cost), the agent extracts the figures from the ticket text.
• It compares them against predefined cost and size thresholds and decides whether to grant automatically or require manager approval. If approval is needed, it tags the designated manager on the ticket.
• If the user opened a ticket without the figures, the agent gives precise instructions to run the restore first and capture the error, so the workflow can proceed.
• Ticket status is updated throughout while awaiting replies – no manual overhead for support.

How It Works in Practice

1. A user opens a ticket, with or without cost and size metrics.
2. The agent checks for the required data and gives guidance if it’s missing.
3. With data present, it compares against thresholds to decide whether manager approval is required.
4. On approval, permissions are granted automatically and the ticket is updated.

The Results

Multiple rounds of back-and-forth between users, support, and managers became a structured, largely automated workflow. No expensive restore happens without explicit approval documented on the ticket – and small, cheap requests proceed without administrative delay.

Why It’s Worth Talking About

An agent orchestrating a real cost-based approval: structured extraction from free text, threshold-based financial logic, and targeted human escalation precisely where money is involved.