Design a Human-Approved AI Support Agent

This guide outlines the process of designing a customer support agent that can utilize bounded tools while ensuring consequential actions pass through a human approval checkpoint. The primary focus is on verified control principles rather than offering unverified framework or model API code for immediate production use.

Key aspects of this design include treating the advanced agent as a privileged digital entity, providing it with a defined owner, clear purpose, and limited permissions. High-impact decisions, such as financial transactions or sensitive data operations, necessitate human approval checkpoints.

The provided example is a fictional retail support workflow where a representative queries about a delayed order. The AI agent can access a permitted order source and an approved policy source, then propose a resolution. However, the actual execution of a refund requires a separate, independently authorized person to review the facts and approve or reject the action.

This document serves as an architectural guide, not a claim that a specific OpenAI model, SDK version, or other technical components will implement the design without further verification. It is essential to confirm the framework and API details against their current official documentation before coding or deployment.

The first step in this process is to define the agent's job and boundaries by creating a narrow job statement. For instance, "Assist an authorized support representative by finding order information and preparing policy-based resolution recommendations." Avoid broad instructions that could lead to misunderstandings or mishandling of customer issues.

Next, document the agent's owner, business purpose, permitted user groups, and the actions it may request. Explicitly separate information access from action authority. For example, the agent may only be permitted to access a specific order for an authenticated case, but not directly modify payment records. Classify actions based on their consequences and reversibility, such as reading an approved order record versus issuing a refund, changing an account, or sending external communications.

Require human review for high-impact actions, and if an action falls outside the approved purpose, the agent should stop and route the case to a human rather than attempting to improvise.

The second step involves mapping the request into a bounded workflow. Sketch the flow from the representative's request to the final outcome. This typically includes authenticating the representative, identifying the case or order in scope, retrieving only permitted information, applying relevant business policy, preparing an explanation or action proposal, requiring review when the action is consequential, and recording the result for future review.

Keep the model's role distinct from the authoritative sources. The model can help interpret natural language requests and prepare a response, while an approved business system should remain the authoritative source for order status, and the organization's applicable policy should determine eligibility and limits. Do not treat generated explanations as evidence that real-world actions occurred.

For a proposed refund, the review screen should present the order or case reference, requested amount, reason, policy basis, and information used to prepare the proposal. If these details are missing, inconsistent, or outside the agent's permitted scope, the workflow should not proceed as though approval has been granted. The reviewer should have a clear way to reject the proposal or take over the case.

Keep the proposal and execution as separate stages. The agent can prepare a request for review, while a separately controlled operation performs the approved action, verifying the reviewer's authorization and ensuring the request still meets current policy.

By following these steps, you can design a customer support agent that effectively utilizes bounded tools while maintaining a critical human approval checkpoint for high-impact decisions.