- Product truth: If the catalog is inconsistent, an agent’s choices will look arbitrary (“the wrong shirt,” “the wrong size,” “the wrong material”), and trust collapses quickly.
- Payee truth: Agentic commerce expands beyond cards to account-to-account and open-banking-connected experiences, broadening the universe of payees and the need to recognize them accurately in real time.
- Identity truth: People operate in multiple contexts (work versus personal). Devices shift. A system that cannot distinguish amongst these contexts will either block legitimate activity or approve risky activity, both of which damage adoption.
This is why unified enterprise data and entity resolution move from nice to have to operationally required. The more autonomy you want, the more you must invest in modern data foundations that ensure it is safe.
Context intelligence: The missing layer
When leaders talk about agentic AI, they often focus on model capability: planning, tool use, and reasoning. Those are necessary, but they are not sufficient.
Agentic commerce also requires a layer that provides authoritative context at runtime. Think of it as a real-time system of context that can answer instantly and consistently:
• Is this the right person?
• Is this the right agent, acting within the right permissions?
• Is this the right merchant or payee?
• What constraints apply right now (budget, policy, risk, loyalty rules, preferred suppliers)?
Two design principles matter.
First, entity truth must be deterministic enough for automation. Large language models are probabilistic by nature. That is helpful for creating options for writing and drawing. It is risky for deciding where money goes, especially in B2B and finance workflows, where “probably correct” is not acceptable.
Second, context must travel at the speed of interaction and remain portable across the entire connected network value chain. Mastercard’s experience optimizing payment flows is instructive: the more services you layer onto a transaction, the more you risk slowing it down. The pattern that scales pre-resolves, curates, and packages the signal so that execution is lightweight.
This is also where tokenization is heading. Initiatives like Mastercard’s Agent Pay and Verifiable Intent signal a future in which consumer credentials, agent identities, permissions, and provable user intent are encoded as cryptographically secure artifacts — enabling merchants, issuers and platforms to deterministically verify authorization and execution at machine speed.