AI Agents and the Future of Real Estate Trading: What Happens When Buildings Become Machine-Readable?

That was one of the central themes of the panel discussion Beyond Tokenization: Building the Data Layer for Real Estate 3.0, where industry leaders explored the relationship between tokenization, data standardization, blockchain infrastructure, and the rise of AI agents.

Their conclusion was clear: before AI can transform real estate investment, the industry must solve the data problem.

The Missing Layer in Real Estate

Real estate remains one of the world's largest asset classes, yet much of its information is fragmented across PDFs, spreadsheets, valuation reports, marketing materials, and proprietary systems.

According to Matthew Schneider (CEO of Building,Inc), private real estate reporting is fundamentally inconsistent. Even when data exists, it often requires interpretation because there is no common standard for how information is collected, presented, or verified.

Unlike public markets, where disclosures are standardized and heavily scrutinized, private real estate still relies on heterogeneous reporting formats that make comparison and analysis difficult.

The result is a market where investors spend significant time interpreting information rather than acting on it.

To unlock scalable investment, the industry needs a common language for real estate data.

Standardization Creates Trust

Several panelists emphasized that standardization alone is not enough. Data must also be verifiable.

Denis Petrovcic (CEO of Blocksquare) described real estate valuation as a complex mix of information that ultimately needs to be distilled into a few critical data points that investors can trust.

Schneider expanded on this idea by arguing that a global market for tokenized real estate requires both standardization and verification. Data must not only circulate between platforms and stakeholders, but also be trusted.

Blockchain can play a key role in this process by creating an immutable verification layer for asset information, valuations, and historical records.

With standardized and verifiable data, investors can compare assets across markets and jurisdictions with greater confidence.

Why AI Agents Depend on Data Quality

As AI agents become increasingly capable, many believe they will eventually assist with investment analysis, portfolio management, and asset operations.

But their effectiveness depends entirely on the quality of the information they receive.

When asked whether AI could improve investment decisions if underlying property data is incomplete, Petrovcic responded with a simple analogy:

His point was straightforward. Incomplete information produces unreliable outputs.

He warned that agents working with incomplete datasets can generate "hallucinated opinions" that differ widely from one another. While AI can help investors process large amounts of information more efficiently, it cannot compensate for missing or inaccurate data.

Schneider agreed, emphasizing that completeness of data is essential because agents only follow the instructions and information they are given. Poor data quality introduces bias, missed opportunities, and flawed conclusions.

In other words, better AI begins with better data.

From Digital Ownership to Digital Buildings

The discussion also moved beyond tokenized ownership to the digitization of the underlying asset itself.

Matthieu Merchadou (CEO of Magma) argued that ownership records alone cannot provide a complete picture of a property's investment potential. While ownership data may show title information or collateral structures, it does not reveal operational risks, capital expenditure requirements, governance considerations, or future performance opportunities.

His vision extends beyond tokenizing ownership toward creating a digital representation of the building throughout its entire lifecycle.

This includes digitizing information about equipment, materials, maintenance history, energy performance, renovations, and operational activities.

Once this information becomes machine-readable, investors gain a much deeper understanding of the asset. At the same time, AI systems can begin extracting intelligence that helps improve building performance, maintenance planning, and investment outcomes.

The goal is not simply to tokenize real estate.

The goal is to digitize the asset itself.

Blockchain's New Role in the Agent Economy

Perhaps the most forward-looking discussion came from Ludovico Rossi (CRO of Brickken), who explored the relationship between blockchain infrastructure and autonomous AI agents.

Rossi argued that blockchain adoption has historically been slower than many expected. However, the rise of AI agents may fundamentally change how blockchain is used.

In his view, blockchain infrastructure becomes significantly more valuable when autonomous agents are the participants.

Why?

Because agents need trusted data, transparent permissions, and auditable records of every action they take.

Rossi described data as the most important pillar of the emerging agent economy. Only with readable and clear information can agents make informed decisions.

But information alone is not enough.

Agents also need a framework that allows them to act.

Introducing ERC-8226 and the Regulated Agent Mandate Standard

To address this challenge, Rossi introduced a new initiative being developed by Brickken: ERC-8226, known as the Regulated Agent Mandate Standard (RAMS).

The purpose of RAMS is to create a standardized way for a person or organization to delegate authority to an AI agent.

Under this model, an agent can act as a compliant proxy, executing actions on behalf of its principal while remaining accountable and traceable.

The standard is designed to support actions such as:

• Trading tokenized securities

• Executing transactions

• Signing actions with legal or financial consequences

• Accessing both on-chain and off-chain information

• Operating within predefined compliance frameworks

Rossi described one of the most challenging future use cases as enabling an agent to compliantly trade on behalf of another individual or entity.

To achieve this, every action must be recorded, identities must be tracked, and decision-making processes must remain auditable.

Blockchain provides the infrastructure for this accountability.

In effect, ERC-8226 aims to become the authorization layer between human intent and autonomous action.

Building Real Estate 3.0

Throughout the discussion, one message repeatedly surfaced: tokenization alone is not enough.

The future of real estate requires a complete data stack.

First comes standardized data.

Then verifiable data.

Then machine-readable assets.

Only after those foundations are established can AI agents reliably analyze opportunities, manage assets, and execute actions on behalf of investors.

The emerging architecture looks something like this:

Data Layer → Verification Layer → Machine Understanding → Agent Decision-Making → Authorized On-Chain Action

Real Estate 3.0 will not be defined by tokens alone.

It will be defined by the quality, accessibility, and trustworthiness of the data that powers them.