AI Agents Don't Know How Addresses Work, and That's a Compliance Problem
An AI agent can draft a contract, reconcile an invoice, and move money across three countries before lunch. What it cannot reliably do is know that a postal code in Japan vs Jordan follows a different logic than one in Sao Paulo, or that a tax identifier valid in Berlin is malformed in Mumbai. This matters when organizations are scaling AI products across global markets, because writing new validation code for every market is not sustainable. The data those agents touch, including addresses, tax identifiers, names, and currency, changes from one jurisdiction to the next, and the rules governing it are not suggestions. They carry the force of law.
Sumeet Ram has spent more than 8 years in the technology industry, building systems across insurance, low-code automation, international lending, and global payments. A Senior Technical Product Manager at one of the world's largest digital payments platforms, she leads internationalization work that spans more than 200 markets, the layer most users never notice until a name is formatted incorrectly or an address field rejects a valid home. For example, in many Asian countries, the family name comes first, whereas in many Western countries, it comes last. Her current focus is what happens when AI agents begin touching that layer directly. The answer, she argues, decides whether automation becomes a compliance asset or a compliance liability.
The Problem With Letting Agents Reason
Large language models do not retrieve facts. They predict text, which means they approximate, and in regulated work approximation carries a measurable failure rate. Models hallucinate on as many as 41% of finance-related queries. A chatbot that invents a plausible transaction or cites a regulation that does not exist is not a rare edge case. It is the predictable behavior of a system asked to guess.
Ram saw the pattern early. When agents are handed locale rules, such as how a Brazilian tax identifier is structured, which characters a Japanese address allows, or where a decimal separator belongs, they often rely on patterns learned during training rather than the deterministic rules defined by an organization's systems. While AI can retrieve and reason over vast amounts of information and may even suggest the correct answer, applying that knowledge consistently within enterprise systems is far more challenging. The output looks confident and is sometimes wrong, and in payments a wrong address or a malformed tax ID does not fail quietly. It surfaces downstream as a rejected transaction, a compliance flag, or a customer locked out of a product that was built for them. Her position was blunt: locale logic is deterministic, and deterministic rules should never be left to a probabilistic model.
"Locale rules are not the kind of thing you want a model to have an opinion about," says Sumeet Ram. "There is a correct answer for how an address works in each market, and the job is to make the agent use it, not approximate it."
Why Close Enough Breaks at the Border
59% of online shoppers now buy from retailers outside their home country, and that demand only converts when the local details are right. A name field that cannot hold the customer's name, currency that displays with the wrong separator, a tax ID in the wrong format: each one reads to the user as a product that was not built for them. The failure is rarely dramatic. It is a rejected form, a flagged record, or a checkout that quietly does not complete.
This is the problem Ram has been working on since joining PayPal over the last 18 months, well before AI agents entered the picture. The internationalization platform she supports handles currency formatting, date and time conventions, address verification, name validation, locale metadata, language and regional preferences, character encoding, number formatting, and many other capabilities across more than 200 markets. Each of those capabilities exists because products are often designed with assumptions from the market where they were originally built, but those assumptions do not hold everywhere. A product designed with a US-first mindset, for example, cannot deliver a native experience in India, just as a platform originally built for Sweden or China must evolve to feel local in every market it serves. The validated functions behind that platform encode years of market-specific rules. The question she set out to answer was how to put that same validated logic in front of an AI agent without letting the agent paraphrase it.
"Globalization gets treated as a chore, something you bolt on at the end," Ram explains. "I have always thought of it as infrastructure. If it is infrastructure, an agent should be able to call it the same way it calls anything else it trusts."
Turning Locale Logic Into Something Agents Can Call
The Model Context Protocol, released as an open standard in late 2024, gave the industry a shared way for AI agents to use external tools instead of reasoning their way through every task. Adoption was fast, and it has since become the default way agentic systems connect to the software around them. The protocol is usually described as plumbing, a cleaner path between an agent and a tool. Its more useful role is as a boundary.
Ram led the work to build her company's first internationalization server on the protocol, scoping it from the standard's earliest public months to a production launch roughly 9 months later with a team of 5 engineers. The design choice that defined the project was narrow and consequential: every tool the server exposes is anchored to an existing, production-validated function. An agent calling the server to validate an address or format a currency does not receive a hint it then completes on its own. It receives the verified answer. The agent stays in the execution path and is kept out of the reasoning path on data that regulators care about.
"The protocol is the integration story everyone tells," Ram notes. "The part I care about is that it can also be a constraint. You can use it to guarantee an agent never improvises on something that has a single right answer."
The Decisions That Keep It Honest
Most agentic projects do not fail on the model. They fail on governance, on unclear value, and on the absence of controls that decide what an agent is and is not allowed to do. The hard part is rarely getting an agent to act. It is drawing the line between the work it can own and the work it must never improvise.
That line is where Ram spent much of her effort. Early proposals for the server included tools that returned partial answers and let the model finish them, and she rejected them, because a half-answered locale rule is the exact place a hallucination hides. Capabilities were prioritized by what an agent actually needs rather than by how much of the underlying system could be exposed, which kept the launch shippable. Discoverability, onboarding documentation, and review with security and compliance partners were scoped as part of the product, because a tool nobody can find or trust does nothing to reduce risk.
"The instinct is to let the model help out where the tool is incomplete," Ram observes. "That instinct is exactly what you have to design against. Either the answer is validated or it is a guess, and on compliance data a guess is a defect."
A Pattern That Reaches Past Addresses
The same logic applies wherever AI meets regulated data. Identity verification, know-your-customer checks, payments, and risk all rest on rules that are jurisdiction-specific, auditable, and unforgiving of error. They are the surfaces where letting an agent approximate would be most tempting, and most dangerous. They are also where a validated tool earns its place.
Ram describes the internationalization server as a template rather than a one-off. The pattern, exposing deterministic validated logic as tools an agent consumes rather than interprets, extends directly to identity, payments, and risk. Inside her company it has already become the reference for how other compliance-sensitive systems should meet AI agents, and the next phase of her work connects these servers to one another so an agent can compose validated capabilities without ever leaving the verified path. The goal is not a smarter agent. It is an agent that knows the difference between a fact it can look up and a fact it should never invent.
"The internet solved connectivity. It did not solve comprehension," Ram reflects. "If we want agents to work for someone in Sao Paulo as well as they do for someone in San Francisco, we have to hand them the rules, not ask them to guess. That is the whole job."
