AI Agent Governance for Airline Loyalty and Refund Systems

Why This Use Case Requires Runtime Governance

Airline loyalty and refund systems sit at the intersection of financial value and customer trust. An AI agent with access to loyalty balances, booking records, and refund tools operates in a domain where a single unauthorized or manipulated action can have direct monetary consequences. Governing that access through prompts or written policy alone assumes the agent will always reason correctly under adversarial conditions, which is not a safe assumption for systems handling financial transactions.

Runtime governance shifts enforcement outside the model itself. Instead of relying on the agent to decline an inappropriate request, a policy enforcement point checks each tool call against defined rules before it reaches PSS, CRM, or payment systems. This distinction matters because prompt-based restrictions can be bypassed through manipulation, while runtime checks operate independently of what the agent believes it should do.

Agent Identity and Permission Architecture

A sound architecture starts with treating the agent as a distinct, scoped identity rather than an extension of a shared service account. Credentials should be short-lived and session-based, issued for a specific task rather than persisting indefinitely. Permissions should be defined at the tool level, so that an agent capable of reading loyalty balances does not automatically inherit the ability to adjust them.

This separation of read and write access, combined with credentials that expire quickly, limits the blast radius of any single compromised or manipulated session. It also makes it possible to reason clearly about what an agent was authorized to do at the moment it acted, rather than relying on broad, standing permissions that were granted for convenience.

The Risk of Chained Tool Calls

Individual tool calls can each appear authorized when viewed in isolation, even when the sequence they form together is not. An agent that reads a customer's booking, then reads their loyalty balance, then initiates a refund may satisfy each system's local authorization check while still completing an action no human intended to approve. Backend systems such as PSS, CRM, and payment gateways typically do not have visibility into the full chain of calls that led to a request reaching them.

This is why a policy enforcement point positioned between the agent and these backend systems is important: it can evaluate the full sequence and context of a request, not just whether the final call matches a permitted pattern. Thresholds for step-up human approval should be enforced at this layer, not left to the agent's own judgment about when a transaction is significant enough to require review.

Auditability and Reconciliation

Effective audit logging captures more than the final API request. It should record the decision context behind each action, including the inputs the agent used and the policy checks that were applied at the time. This level of detail is what allows a governance or security team to reconstruct why an action occurred, not just that it occurred.

Because loyalty and refund workflows span multiple systems, logs generated in PSS, CRM, and payment platforms need to be centralized to support reconciliation of the full action chain. Without this, an anomaly detected in one system may be difficult to trace back to the agent session or tool call that caused it.

Evaluation Questions for Governance Leaders

• Are agent credentials scoped to specific tools and short-lived, or are they long-lived keys shared across multiple agents or sessions?
• Is there a policy enforcement point independent of the agent that validates tool calls before they reach PSS, CRM, or payment systems?
• Does the architecture require human approval for refund or loyalty adjustments above a defined financial threshold, enforced outside the agent's reasoning?
• Are audit logs capturing decision context, including inputs and policy checks, rather than only the final transaction record?
• Are logs from PSS, CRM, and payment systems centralized so a chained sequence of calls can be reconstructed and reviewed?
• How frequently are agent credentials and scopes reviewed and rotated relative to other automated-account access practices?