The problem
When a consumer asks an AI agent to find the best price on a product, the agent needs to know what the consumer's loyalty status is worth: the points they would earn, the discount their tier provides, the offer that applies to this basket. Most programs cannot answer, because the terms exist as prose on a website and as logic inside a platform nobody outside engineering can query. The agent sees a sticker price, and the brand's investment in loyalty is invisible at the moment of decision.
What a machine-readable program requires
| Requirement | Why it matters |
|---|---|
| Structured rules | Earn rates, tier thresholds and offer eligibility as explicit configuration, not narrative |
| Real-time evaluation APIs | An agent needs "what would this basket earn for this member" answered within the interaction |
| Consent and identity controls | A member must authorize an agent to act on their account; the platform must scope what the agent can read and do |
| Deterministic outcomes | What the agent was told will happen must be what the register does |
| Governance and audit | Agent activity is logged like any other channel; abuse is detectable and reversible |
How ReactorCX approaches it
ReactorCX was built API-first with program logic in structured configuration, so the same interfaces that serve a point-of-sale system or a mobile app can serve an authorized agent. The RCX AI Server exposes program configuration through the Model Context Protocol so that AI systems read the program the way an engineer would, inside the platform's permissions, approval workflows and audit framework. The deterministic engine returns the same answer to an agent that it returns to the register, because it is the same evaluation.
The governance model matters more here than anywhere else. An agent acting for a consumer is a new channel with a new fraud surface; it gets the same scoped access, rate controls, audit trail and reversal path as every other channel. Read Built for AI, before AI for the operating model.
What to ask a vendor
- Can an authorized third party evaluate what a basket would earn for a member, in real time, through a documented API?
- Are program rules available as structured configuration, or only as documentation?
- How is agent access scoped, consented, logged and revoked?
- Is the answer an agent receives the same one the engine executes at the transaction?