# Agent-Ready Market Infrastructure **Verified Representation, Computational Eligibility, and Global Market Access in AI-Mediated Economies** --- **Published Working Paper — Version 1.0** **DOI:** 10.5281/zenodo.21241637 **Zenodo:** https://zenodo.org/records/21241637 --- **Version 1.0** | **July 7, 2026** **Author:** Marco Patrone **Affiliation:** HomeSelf / Representation Economy Research **Research Program:** Representation Economy Research Program **Volume:** Volume VIII **Sequence:** 17 --- ## ⚠️ Disclaimer This working paper proposes a conceptual framework intended for further empirical validation. The indicators and formulas introduced are designed as analytical tools and should not be interpreted as finalized regulatory standards, legal advice, financial advice, or investment advice. This is theoretical research; all concepts, formulas, and conclusions require empirical validation. --- ## Abstract This paper introduces Agent-Ready Market Infrastructure as an emerging category of economic infrastructure for AI-mediated markets. As artificial agents increasingly mediate discovery, comparison, ranking, verification, negotiation, and transaction initiation, market participation will depend not only on being online, but on being represented in forms that machines can interpret and act upon. We argue that AI-mediated markets are structurally global: once demand is mediated by artificial agents, economic objects can be compared across jurisdictions, platforms, languages, and institutional systems. This creates a global computational market in which firms, assets, and services require verified, machine-readable, comparable, permissioned, and transaction-capable representations. We define Agent-Ready Market Infrastructure as the institutional, technical, and representational layer that enables economic entities, assets, and services to become discoverable, interpretable, comparable, verifiable, permissioned, and actionable for AI agents operating across jurisdictions. We introduce the Agent-Readiness Index (ARI) as a measurement framework with six conditions—Discoverability, Interpretability, Comparability, Verifiability, Permissioned Access, and Transaction Capability—and extend it into the Global Agent-Readiness Index (GARI) incorporating jurisdictional legibility and semantic portability. The paper positions Representation Capital as the input layer of agent-ready markets and Computational Eligibility as a condition for market access in AI-mediated economies. Real estate is examined as a critical test case because property markets are high-value, document-heavy, jurisdiction-dependent, trust-sensitive, and globally comparable. We argue that agent-ready property records can transform human-facing listings into verified, machine-readable, and transaction-capable market objects. The paper concludes that the next layer of market globalization will be shaped significantly by the ability of artificial agents to discover, compare, verify, and initiate transactions across economic objects represented in computable form. --- ## Keywords Agent-Ready Market Infrastructure; AI-Mediated Markets; Representation Economy; Representation Capital; Computational Eligibility; Computational Sovereignty; Global Market Access; AI Governance; Real Estate; Machine-Readable Assets; Global Agent-Readiness Index; Jurisdictional Legibility; Semantic Portability; Agentic Web; Computational Market Infrastructure; Verified Property Records; Agent-Readable Property Markets. --- ## 1. Introduction: From Digital Markets to Agent-Ready Markets The historical architecture of digital markets operated on visibility assumptions: being online meant being potentially findable, being findable meant being potentially considered, and being considered meant the possibility of transaction. This model shaped market infrastructure for three decades—search engines optimized for human queries, platforms organized for human browsing, and listings designed for human visual comparison. Under this architecture, market access was primarily a function of visibility. Firms invested in search engine optimization, paid placement, featured listings, and platform positioning. The assumption was that visibility was the binding constraint: if economic actors could make their assets visible to human searchers, those assets would be considered for transaction. Artificial intelligence mediates demand differently. When AI systems construct consideration sets before ranking, when they compare options across attributes rather than positions, and when they initiate transactions on behalf of human principals, the infrastructure conditions for market participation change fundamentally. Visibility remains relevant for human-directed search. However, as AI systems mediate more economic demand, a deeper condition emerges: the economic object must be represented in forms that machines can interpret and act upon. Being visible on a platform is no longer sufficient if the underlying representation is not machine-readable. Being listed on a website is no longer sufficient if the listing cannot be parsed, verified, compared, and acted upon programmatically. The question is no longer whether an asset is online. The question is whether it is agent-ready. An asset is online when it has a URL. An asset is agent-ready when it can be discovered, interpreted, compared, verified, permissioned, and acted upon by artificial agents operating across jurisdictions. These are not the same condition. This paper argues that AI-mediated markets are structurally global by default. Once demand is mediated by AI agents, the bounds of discovery, comparison, and transaction initiation are no longer geographically constrained. Agents can operate across platforms, jurisdictions, languages, and institutional systems. This creates a global computational market in which participation depends less on physical proximity, advertising spend, or platform placement, and more on whether economic entities are machine-readable, verifiable, comparable, permissioned, and actionable. We introduce Agent-Ready Market Infrastructure as the institutional, technical, and representational layer that enables this transition. We define the Agent-Readiness Index (ARI) as a measurement framework, extend it into the Global Agent-Readiness Index (GARI) for cross-jurisdictional markets, and examine real estate as a critical test case. --- ## 2. The Global Governance Context Current global AI governance debates increasingly focus on the institutional questions raised by AI-mediated economic activity. Initiatives including the United Nations Global Dialogue on AI Governance (ongoing since 2023) and the AI for Good Global Summit (convened annually by ITU since 2017) reflect growing recognition that AI governance extends beyond model safety, ethics, disinformation, and concentration to include questions of infrastructure, inclusion, and equitable access. [NOTE: These initiatives are mentioned as contextual background only; no official affiliation, endorsement, or partnership is claimed.] This paper connects to that broader context. The argument is that current governance debates under-address three questions: 1. **Market Access**: If artificial agents mediate economic demand, what infrastructure conditions determine which firms, assets, and services can participate? 2. **Representation Infrastructure**: What institutional, technical, and representational layers are required for economic objects to become computationally eligible? 3. **Cross-Jurisdictional Legibility**: How can markets ensure that computational eligibility extends across borders, languages, and regulatory systems? These questions are not merely technical. They are questions of economic infrastructure comparable to financial access, network access, or identity access. Participation in AI-mediated markets may become infrastructure-dependent in ways that policy must address. The governance challenge is that Agent-Ready Market Infrastructure cuts across traditional institutional boundaries. It involves data standards (technical domain), market regulation (economic domain), and cross-border coordination (diplomatic domain). No single existing institution has clear authority or mandate. This creates a risk of fragmented, uncoordinated development where different jurisdictions, platforms, and operators establish incompatible representation standards. Fragmentation would create new barriers to entry and new forms of infrastructure dependency—precisely the outcomes that global governance should seek to prevent. --- ## 3. Why AI-Mediated Markets Are Structurally Global AI-mediated markets differ structurally from web-mediated markets in ways that create global default conditions. The web retained geographic boundaries because discovery was primarily human-directed and language-constrained. AI-mediated discovery operates differently. ### 3.1 Discovery Without Geographic Boundaries When discovery is mediated by AI agents, geographic proximity is no longer the primary constraint. An AI agent tasked with finding "commercial properties in European capitals with ESG ratings above X and yields above Y" can operate across all jurisdictions simultaneously. The agent does not distinguish whether a property is in France, Germany, or Spain—it cares whether the property is represented in computable form. This differs fundamentally from human-mediated search, where language, local knowledge, and navigation friction create practical boundaries. An AI agent fluent in multiple languages and capable of parsing multiple jurisdictional codes faces no such friction. The implication is that AI-mediated markets default to global scope. The agent's consideration set is defined by computational criteria, not geographic proximity. Any economic object that meets the criteria and is represented in machine-readable form is candidate for inclusion—regardless of jurisdiction. ### 3.2 Comparison Across Jurisdictions AI agents can compare options across different regulatory, tax, and legal systems. A buyer does not need to know each country's property laws—the agent can retrieve, interpret, and compare relevant constraints across jurisdictions. This capability creates a new form of cross-jurisdictional competition. Economic objects are compared not only on price and quality, but on the entire bundle of jurisdictional conditions—tax treatment, ownership restrictions, compliance requirements, enforcement mechanisms, transaction costs. The implication is that jurisdictional legibility becomes a component of market access. Jurisdictions that make their rules, codes, and compliance requirements machine-readable become more accessible to AI-mediated discovery. Jurisdictions that keep rules embedded in human-readable documents, ambiguous language, or distributed sources face structural disadvantage. ### 3.3 Transaction Initiation at Scale AI agents can initiate transactions across borders. They can submit inquiries, negotiate terms, and coordinate settlement processes without human intermediation. This makes transaction capability a prerequisite for market participation, not an afterthought. The capability to initiate transactions at scale creates new dynamics. An AI agent can simultaneously submit inquiries for hundreds of properties across multiple jurisdictions, filter responses based on machine-readable criteria, and proceed to offer submission without human intervention until final commitment. This capability transforms market efficiency but also creates new risks. The agent requires verifiable representation to avoid fraud, permission structures to ensure authorized action, and transaction protocols to coordinate multi-step processes across legal systems. ### 3.4 The Structural Globality Claim The claim that AI-mediated markets are structurally global follows from these three capabilities. When discovery, comparison, and transaction initiation are all mediated by AI agents: - Geographic boundaries are irrelevant to search and consideration - Jurisdictional differences are parameters in comparison, not barriers to access - Transaction capability can be exercised across borders without physical presence The primary constraints are not borders, languages, or currencies—they are whether economic objects are represented in forms that machines can use. --- ## 4. The Limits of Websites, Platforms, and Human-Readable Listings Websites, platforms, and human-readable listings were designed for human visual consumption. They optimize for visual presentation, platform ranking, and human navigation. These design choices create structural limitations for AI-mediated discovery. ### 4.1 What Web Markets Optimized For The historical architecture of web markets optimized for three conditions: 1. **Visual presentation**: Images, layout, typography designed for human eyes. Property listings emphasize photography, floor plans, and visual appeal because humans process visual information rapidly. 2. **Platform ranking**: SEO, paid placement, featured listings designed to position assets within human-visible hierarchies. Being on page one of search results or featured in "recommended" sections drove visibility. 3. **Human navigation**: Categories, filters, search bars designed for manual browsing. Users click through categories, apply filters, and scan results—human-scale interaction patterns. This architecture served human-mediated markets well. However, it assumes that the primary consumer of information is a human visual system. When AI agents become primary consumers of market information, the architecture misaligns with requirements. ### 4.2 Structural Limitations for AI-Mediated Discovery Human-readable listings create five structural limitations for AI-mediated discovery: 1. **Unstructured or semi-structured data**: Information embedded in HTML, images, or PDFs is difficult for machines to extract reliably. A property description written as prose text cannot be easily parsed into structured attributes. 2. **Inconsistent schemas**: Different platforms use different attribute names, units, and conventions. What one platform calls "square footage," another calls "living area" in square meters; what one calls "annual yield," another calls "cap rate" with different calculation methods. 3. **Missing verification signals**: Marketing claims lack machine-readable evidence sources. A listing can claim "recently renovated" without providing verifiable documentation, permits, or timestamped evidence. 4. **No explicit permission structures**: Terms and conditions are written for humans, not machines. An AI agent cannot programmatically determine what actions are permitted without machine-readable policy definitions. 5. **No transaction protocols**: Contact forms and call-to-action buttons require human intervention. An AI agent cannot submit an offer with structured terms, track status, or coordinate next steps without defined protocols. ### 4.3 The Visibility-Readiness Gap A key insight is that visibility and agent-readiness are not the same condition. An asset may be highly visible on a platform—featured listing, premium placement, high search rankings—yet remain invisible to AI agents if its representation is not machine-readable. Conversely, an asset with excellent machine-readable representation may be discoverable by AI agents regardless of its platform position. This creates a divergence between web optimization and agent-readiness optimization. Web optimization focuses on placement within human-visible hierarchies. Agent-readiness optimization focuses on representation quality, verification signals, and transaction protocols. As AI systems mediate more economic demand, the visibility-readiness gap becomes a source of structural advantage and disadvantage. Assets optimized for visibility but not readiness may experience declining inclusion in AI-constructed consideration sets. Assets optimized for readiness may experience inclusion regardless of platform position. --- ## 5. Market Architecture: Web vs Agent-Ready The transition from web-mediated to agent-ready markets represents a fundamental shift in market architecture. This shift can be characterized through the flow from discovery to transaction. ### 5.1 Web-Mediated Market Flow In web-mediated markets, the typical flow is: 1. **Local visibility**: Asset appears on platform, directory, or search engine within geographic or language-constrained scope 2. **Platform ranking**: Asset positioned within search results based on SEO, paid placement, or platform algorithms 3. **Human comparison**: Potential buyer manually compares options by browsing listings, viewing images, reading descriptions 4. **Manual transaction**: Buyer contacts seller, negotiates terms, coordinates transaction through offline processes This flow assumes human agency at each stage. Discovery is constrained by human-scale search and navigation. Comparison is constrained by human visual processing and manual browsing. Transaction requires human-to-human communication and coordination. ### 5.2 Agent-Ready Market Flow In agent-ready markets, the flow becomes: 1. **Computable representation**: Asset represented in machine-readable form with canonical identifiers, structured attributes, and verification signals 2. **AI discovery**: AI agents search across global scope using computational criteria rather than geographic constraints 3. **Machine comparison**: AI agents compare options across attributes using standardized schemas, machine-readable units, and verifiable claims 4. **Verification**: AI agents validate claims through linked evidence sources, digital signatures, and authoritative record references 5. **Transaction initiation**: AI agents submit inquiries, negotiate within defined protocols, and coordinate settlement processes This flow assumes machine agency at each stage except final commitment. Discovery is global and criteria-based rather than local and ranking-based. Comparison is automated across attributes rather than manual across visual presentations. Verification is computational rather than trust-based. Transaction initiation is protocol-based rather than communication-based. ### 5.3 The Infrastructure Implications The different flows imply different infrastructure requirements. Web-mediated markets require: - Hosting and bandwidth for visual content - Search and directory infrastructure - Payment processing and basic user authentication Agent-ready markets require: - Canonical identifiers and registries - Structured data schemas and standards - Verification infrastructure and evidence sources - Machine-readable permission structures - Transaction protocols and coordination systems The new infrastructure requirements are not incremental improvements to web infrastructure. They represent a distinct layer of market infrastructure that enables computable representation and machine-mediated transaction. --- ## 6. Definition of Agent-Ready Market Infrastructure **Agent-Ready Market Infrastructure** is the institutional, technical, and representational layer that enables economic entities, assets, and services to become discoverable, interpretable, comparable, verifiable, permissioned, and transaction-capable for AI agents operating across jurisdictions. This definition has three components: 1. **Institutional**: Governance frameworks, standards, and policies that define what counts as valid representation, how disputes are resolved, and who operates registries and verification systems. 2. **Technical**: Protocols, schemas, APIs, and verification systems that enable machine-to-machine communication, secure authentication, and coordinated transaction processes. 3. **Representational**: The actual machine-readable records, canonical identifiers, and evidence sources that describe economic objects in computable form. Agent-Ready Market Infrastructure is not merely better data or additional APIs. It is a coordinated system across institutional, technical, and representational layers that enables economic participation in AI-mediated markets. The institutional layer cannot be reduced to the technical layer because questions of governance, authority, and dispute resolution involve human judgment and political processes. The technical layer cannot be reduced to the representational layer because protocols, APIs, and verification systems are required to enable interaction, not just static representation. The representational layer cannot be reduced to either institutional or technical layers because the quality of machine-readable records determines what is computationally possible. All three layers are required. Absent institutional frameworks, technical systems lack legitimacy and dispute resolution mechanisms. Absent technical protocols, representational records cannot interact. Absent representational quality, institutional and technical investments cannot enable computational access. --- ## 7. The Six Conditions of Agent-Readiness We introduce the **Agent-Readiness Index (ARI)** as a framework for measuring whether an economic object is agent-ready. The index has six conditions, each representing a necessary dimension of computational eligibility. ### 7.1 Discoverability (D) AI agents must be able to find the economic object through computational search. Discoverability requires: - **Stable, canonical identifiers**: The object must have persistent identifiers that do not change across platforms or over time. URLs change, platforms restructure, but canonical identifiers enable consistent reference. - **Machine-readable index or registry**: There must be a computable index that agents can query to find objects matching criteria. Human-browsable directories are insufficient. - **Search interface optimized for automated query**: The search interface must support programmatic access with structured queries, not human-oriented search boxes. Discoverability is distinct from visibility. An object may be visible to humans on a website but not discoverable by AI agents if it lacks canonical identifiers or machine-readable search interfaces. ### 7.2 Interpretability (I) The object must be represented in forms that AI systems can parse and understand. Interpretability requires: - **Structured data with explicit attribute names and types**: Properties must be represented as key-value pairs with explicit types (string, number, boolean, date), not as prose text. - **Clear relationships between entities**: Ownership, encumbrances, permissions, and other relationships must be explicitly represented, not implied. - **Machine-readable definitions and units**: Terms must be defined using standard vocabularies; units must be explicit and convertible. Interpretability enables AI agents to process representations without ambiguity. When data is unstructured or ambiguous, agents must rely on inference, which introduces error and risk. ### 7.3 Comparability (C) Attributes must be structured to enable machine comparison across alternatives. Comparability requires: - **Standardized attribute schemas across the market**: Different objects must use the same attribute names and definitions for equivalent concepts. - **Common units and measurement conventions**: Measurements must use standard units or provide conversion functions. - **Normalization functions for heterogeneous data**: When data varies in format or scale, normalization functions enable comparison. Comparability enables agents to rank and filter alternatives based on criteria. Without standardized schemas and units, comparison requires manual mapping and interpretation, which undermines the purpose of automated search. ### 7.4 Verifiability (V) Claims about the object must be verifiable through trusted evidence sources. Verifiability requires: - **Digital signatures and cryptographic proofs where applicable**: Representations should be signed by authoritative sources, enabling verification of origin and integrity. - **Links to authoritative sources**: Claims should reference primary sources—registries, certificates, official records—rather than being unverified assertions. - **Evidence-based claims rather than unverified assertions**: Every material claim should have a traceable evidence path. Verifiability enables agents to distinguish marketing claims from verified facts. In high-value transactions like real estate, the cost of fraud makes verifiability essential for agent-mediated participation. ### 7.5 Permissioned Access (P) AI agents must understand what actions are permitted and be able to act within authorization boundaries. Permissioned access requires: - **Machine-readable terms, conditions, and policies**: Permissions must be expressed in computable form, not as legal prose. - **Explicit authorization scopes for different agent types**: Different agent classes may have different permissions; these must be defined explicitly. - **Standardized permission query interfaces**: Agents must be able to query what actions are permitted before attempting them. Permissioned access enables agents to act on behalf of human principals within defined boundaries. Without machine-readable permission structures, agents cannot safely initiate actions. ### 7.6 Transaction Capability (T) The object must support AI-mediated transaction initiation. Transaction capability requires: - **Structured interfaces for inquiries, offers, and acceptance**: Communication must follow defined protocols with message formats, not unstructured messages. - **Coordination protocols for multi-step transactions**: Real estate transactions involve multiple steps—offer, acceptance, due diligence, closing—requiring coordination protocols. - **Integration with settlement and fulfillment systems**: Transaction protocols must interface with payment, title transfer, and fulfillment systems. Transaction capability enables agents to initiate and coordinate transactions without constant human intervention. Without structured protocols, agents cannot reliably manage multi-step processes. ### 7.7 The Multiplicative Structure The Agent-Readiness Index uses a multiplicative formula: `` ARI(e) = D(e) × I(e) × C(e) × V(e) × P(e) × T(e) `` where *e* is an economic entity or asset and each term is a value between 0 (absent) and 1 (fully present). The multiplicative structure matters because if any dimension is zero, agent-readiness is zero. An asset may be discoverable, interpretable, comparable, and verifiable, but if it lacks transaction capability, it is not agent-ready. All six conditions are necessary. This structure captures a key insight: agent-readiness is a threshold condition, not an incremental improvement. Being partially ready is not sufficient—the infrastructure must enable the entire flow from discovery to transaction. --- ## 8. Global Agent-Readiness and Cross-Jurisdictional Legibility The Agent-Readiness Index measures whether an economic object is ready for AI-mediated interaction. However, AI-mediated markets are global. An asset must be legible to artificial agents comparing opportunities across jurisdictions, languages, regulatory systems, tax regimes, ownership rules, compliance regimes, trust frameworks, and transaction conditions. ### 8.1 The Global Agent-Readiness Index (GARI) We extend ARI into the **Global Agent-Readiness Index (GARI)**: `` GARI(e, j) = ARI(e) × J(e, j) × S(e) `` where: - ***e*** is the economic entity or asset - ***j*** is the jurisdictional context - ***J(e, j)*** is jurisdictional legibility—the extent to which the asset can be understood across different legal, regulatory, and compliance systems - ***S(e)*** is semantic portability—the ability of representation to be meaningfully compared across languages, standards, and market conventions ### 8.2 Jurisdictional Legibility (J) Jurisdictional legibility captures the extent to which an economic object can be understood across different legal and regulatory systems. This includes: - **Property rights**: Ownership structures, title systems, encumbrance regimes - **Usage restrictions**: Zoning, building codes, permitted uses - **Tax obligations**: Property taxes, transaction taxes, withholding rules - **Compliance requirements**: Local regulations, permitting, reporting For AI agents to compare properties across jurisdictions, these rules must be represented in machine-readable form. A French property and a German property may have identical physical attributes, but different tax treatments, ownership rules, and compliance requirements create different economic outcomes. Jurisdictional legibility enables agents to incorporate these differences into comparison. ### 8.3 Semantic Portability (S) Semantic portability captures the ability of representation to be meaningfully compared across languages, standards, and market conventions. This includes: - **Vocabulary standardization**: Using standard terms rather than local synonyms - **Unit consistency**: Standard measurement units or explicit conversion functions - **Definition clarity**: Explicit definitions that enable cross-cultural interpretation A property listed as "1000 square feet" in one market and "100 square meters" in another requires unit conversion for comparison. A property listed with "yield" calculated differently across markets requires definition normalization for meaningful comparison. Semantic portability enables these conversions and normalizations. ### 8.4 The Global Scope Implication The GARI framework captures the global scope of AI-mediated markets. An asset may have high ARI within its local market but low GARI globally because its representation lacks jurisdictional legibility or semantic portability. As AI agents increasingly mediate cross-border investment and comparison, GARI becomes the relevant metric for market access. --- ## 9. Representation Capital as the Input Layer Representation Capital, introduced in Volume I of this research program, is the accumulated stock of machine-readable qualities that increases computational admissibility probability. Representation Capital can be understood as the input layer of Agent-Ready Market Infrastructure. ### 9.1 The Relationship Between Representation Capital and ARI Where ARI and GARI measure agent-readiness at a point in time, Representation Capital measures the accumulated investment in representation quality over time. High Representation Capital enables high ARI scores; low Representation Capital constrains agent-readiness regardless of infrastructure investment. Representation Capital accumulates through: - Structuring data in machine-readable formats - Establishing canonical identifiers - Creating verification records and evidence links - Normalizing attributes to standard schemas - Building permission structures and transaction protocols Each investment in representation quality increases the stock of Representation Capital, which in turn enables higher agent-readiness scores. ### 9.2 The Compounding Dynamics The relationship between Representation Capital and agent-readiness creates compounding dynamics. Economic objects with high Representation Capital are more discoverable, interpretable, and comparable to AI agents, which increases their inclusion in consideration sets, which creates more transaction data, which can be fed back into representation quality, further increasing Representation Capital. Conversely, economic objects with low Representation Capital may be systematically excluded from AI-mediated consideration, creating less transaction data and fewer opportunities to build Representation Capital, further constraining their future agent-readiness. This dynamic suggests that early investment in representation quality creates compounding advantages, while non-investment creates structural disadvantages. As AI-mediated markets grow, Representation Capital advantages compound, creating structural inequalities between early and late adopters of agent-ready infrastructure. ### 9.3 Policy Implications The compounding dynamics of Representation Capital have policy implications. If market access in AI-mediated economies depends on representation infrastructure, and representation advantages compound over time, then early non-participation creates structural exclusion that is difficult to reverse. This suggests that Representation Capital may become a new axis of economic inequality—comparable to financial capital, human capital, or social capital. Policy may need to address unequal access to representation infrastructure to prevent structural exclusion. --- ## 10. Computational Eligibility **Computational Eligibility** is the condition of being discoverable, interpretable, comparable, verifiable, permissioned, and acted upon by artificial agents within a relevant institutional and jurisdictional context. ### 10.1 From Market Access to Computational Eligibility Computational Eligibility reframes market access for AI-mediated economies. In historical markets, access was determined by physical proximity, network access, or financial access. In AI-mediated markets, access is determined by computational eligibility. The primary risk is no longer being undervalued or outranked—it is being computationally excluded from consideration entirely. An asset may exist, have value, and be transactable, yet never appear in AI-constructed consideration sets because it lacks the representation quality required for computational eligibility. ### 10.2 Structural vs. Cyclical Exclusion Computational eligibility creates a distinction between structural and cyclical exclusion: - **Cyclical exclusion**: Temporary exclusion due to market conditions, ranking algorithms, or platform positioning. Can be reversed through investment in visibility or changes in market conditions. - **Structural exclusion**: Persistent exclusion due to lack of representation infrastructure. Cannot be reversed without investment in machine-readable representation, verification systems, and transaction protocols. As AI systems mediate more economic demand, the risk shifts from cyclical to structural exclusion. Assets that lack agent-ready representation may become systematically excluded regardless of market conditions. ### 10.3 Infrastructure Dependency Computational eligibility makes market access infrastructure-dependent. Participation requires representation infrastructure, not just websites or platform listings. This creates new forms of infrastructure dependency: - **Dependency on registries**: Economic objects require canonical identifiers from authoritative registries - **Dependency on verification systems**: Claims require verification infrastructure for credibility - **Dependency on standards**: Comparability requires adherence to standard schemas - **Dependency on protocols**: Transaction capability requires standardized protocols Infrastructure dependency creates new forms of market power. Operators of registries, verification systems, standards bodies, and protocol providers gain structural influence over market access. --- ## 11. Verification and Trust in Agent-Mediated Transactions Trust in AI-mediated transactions requires verifiable representation. When humans transact, they use institutional trust mechanisms—brand reputation, regulatory oversight, legal recourse. When AI agents transact on behalf of humans, they require machine-readable verification. ### 11.1 The Trust Problem in Agent-Mediated Transactions AI agents cannot rely on human-oriented trust mechanisms because they cannot assess brand reputation, interpret regulatory oversight, or pursue legal recourse. They require computational trust mechanisms based on verifiable representation. The trust problem has four dimensions: 1. **Source verification**: Confirming that data comes from authoritative sources, not spoofed or manipulated 2. **Integrity verification**: Confirming that data has not been tampered with in transmission or storage 3. **Validity verification**: Confirming that claims are supported by evidence from authoritative sources 4. **Authorization verification**: Confirming that the entity has permission to transact ### 11.2 Verification Protocols Agent-Ready Market Infrastructure includes verification protocols that address these dimensions: - **Digital signatures**: Representations signed by authoritative sources enable source and integrity verification - **Evidence links**: Claims linked to primary sources (registries, certificates, official records) enable validity verification - **Authorization tokens**: Cryptographic tokens from authorization systems enable permission verification These protocols enable computational trust. AI agents can verify that representations come from authoritative sources, have not been tampered with, are supported by evidence, and are authorized for transaction. ### 11.3 Verified Property Records Verified Property Records are one implementation of these verification protocols for real estate. A verified property record includes: - **Canonical identity**: Persistent identifier from authoritative registry - **Structured attributes**: Property facts in standardized schema - **Digital signatures**: Signatures from authoritative sources (land registry, cadastral office) - **Evidence links**: References to primary sources (title documents, zoning records, permits) - **Authorization status**: Current ownership and encumbrance information from authoritative sources Verified Property Records enable AI agents to verify property claims before initiating transactions, reducing fraud risk and enabling reliable agent-mediated participation. --- ## 12. Transaction-Capable Economic Objects An economic object is transaction-capable when AI agents can initiate, coordinate, and complete transactions on behalf of human principals. This requires more than data—it requires protocols. ### 12.1 From Data to Protocols Machine-readable data enables discovery and comparison. Transaction capability requires protocols that enable action. The distinction is crucial: - **Data**: Static representation of facts and attributes - **Protocols**: Defined patterns of interaction for coordinated action An asset may have excellent data representation but lack transaction protocols. The asset can be discovered and compared but cannot be acted upon by AI agents. ### 12.2 Transaction Protocol Requirements Agent-Ready Market Infrastructure includes transaction protocols that enable: 1. **Inquiry**: Agents can query availability, terms, and conditions using structured message formats 2. **Offer**: Agents can submit proposals with machine-readable terms, conditions, and timelines 3. **Negotiation**: Agents can engage in structured negotiation within defined rules and counter-offer protocols 4. **Acceptance**: Parties can accept offers with cryptographic commitment that prevents withdrawal 5. **Settlement**: Agents can coordinate with payment, title transfer, and fulfillment systems These protocols must be machine-readable, standardized, and integrated across the market ecosystem. Without standardization, agents cannot interact across different parties and platforms. ### 12.3 Real Estate Transaction Complexity Real estate exemplifies why transaction protocols matter. Property transactions involve: - **Due diligence**: Verification of title, zoning, permits, encumbrances - **Financing**: Mortgage applications, approvals, and coordination - **Legal processes**: Contracts, contingencies, and closing procedures - **Settlement**: Payment coordination, title transfer, recording Each step involves coordination between multiple parties with different interests and timelines. Transaction protocols enable AI agents to coordinate this complexity without constant human intervention. --- ## 13. Why Real Estate Is a Critical Test Case Real estate is one of the first sectors where Agent-Ready Market Infrastructure becomes necessary because property markets are high-value, document-heavy, jurisdiction-dependent, trust-sensitive, and globally comparable. ### 13.1 High-Value Transactions Real estate transactions involve substantial capital. Single properties represent years of household income or significant corporate investment. The allocative cost of exclusion is high—systematic exclusion from consideration for high-value transactions has meaningful economic impact. This makes real estate a critical test case for agent-readiness. If infrastructure-dependent market access emerges in any sector, it will emerge first in high-value sectors where the cost of exclusion justifies investment in representation infrastructure. ### 13.2 Document-Heavy Records Property records span multiple dimensions—ownership, zoning, building codes, tax status, encumbrances, permits, and more. These records are distributed across multiple sources, often in human-readable formats, and may exist only as physical documents or scanned PDFs. The document-heavy nature of real estate creates representation challenges. Transforming these records into machine-readable, verifiable, and comparable forms requires significant investment in infrastructure. This challenge makes real estate a revealing test case. If Agent-Ready Market Infrastructure can be implemented for document-heavy property records, it can likely be implemented for other asset classes with simpler representation requirements. ### 13.3 Jurisdiction-Dependent Rules Each property operates within specific legal, regulatory, and compliance systems. Property rights, tax treatment, ownership rules, and compliance requirements vary by jurisdiction. A property in France faces different rules than an equivalent property in Germany or Spain. The jurisdiction-dependent nature of real estate makes it a critical test case for jurisdictional legibility. If AI agents can compare properties across different legal and regulatory systems, they can likely handle other cross-jurisdictional comparisons. ### 13.4 Trust-Sensitive Transactions Buyers cannot inspect properties physically before consideration in many cases, especially for cross-border investment. This creates inherent information asymmetry and fraud risk. Trust becomes a central concern. The trust-sensitive nature of real estate makes verification infrastructure essential. Without verified representation, AI agents cannot reliably participate in property markets on behalf of remote investors. ### 13.5 Globally Comparable Investment International investors compare properties across countries using yield, appreciation potential, tax treatment, and stability criteria. This cross-border comparison is already happening, mediated by human analysts and advisors. The global comparability of real estate makes it a leading indicator for agent-readiness. As AI systems increasingly mediate this cross-border comparison, the demand for agent-ready property records will grow faster than in more localized markets. ### 13.6 Summary: The Critical Test Case Real estate combines all the characteristics that make Agent-Ready Market Infrastructure necessary: high value, complex records, jurisdiction dependence, trust sensitivity, and global comparability. It is therefore a critical test case for the broader transition to agent-ready markets. If Agent-Ready Market Infrastructure can be successfully implemented for real estate, the lessons learned will inform implementation for other asset classes. If it cannot, the structural challenges may be more severe than anticipated. --- ## 14. Agent-Ready Property Records Agent-ready property records transform human-facing listings into verified, machine-readable, and transaction-capable market objects. They represent the implementation of Agent-Ready Market Infrastructure for the real estate sector. ### 14.1 Components of Agent-Ready Property Records Agent-ready property records include: 1. **Canonical identity**: Stable identifiers that persist across platforms and over time, derived from authoritative sources (cadastral codes, parcel IDs) 2. **Structured attributes**: Property facts in standardized schemas including location, size, rooms, condition, amenities, and other relevant attributes 3. **Verification signals**: Digital signatures from authoritative sources (land registry, cadastral office), links to primary documents, evidence trails for material claims 4. **Action protocols**: Machine-readable terms for inquiries, offers, and transactions, including acceptance rules and coordination procedures 5. **Jurisdictional mapping**: Links to local regulations, tax codes, zoning rules, and compliance requirements in machine-readable form 6. **Semantic portability**: Standard vocabularies and units enabling cross-border comparison, with conversion functions for local variations ### 14.2 From Listings to Market Objects The transformation from human-facing listings to agent-ready market objects represents a fundamental shift in property representation. Human-facing listings emphasize visual appeal, marketing language, and platform positioning. Agent-ready market objects emphasize structured data, verification, and transaction protocols. This transformation does not eliminate the need for human-facing presentation. Rather, it creates a dual-layer system: agent-ready records as the canonical source, with human-facing presentations derived from and synchronized with those records. ### 14.3 HomeSelf as Vertical Implementation HomeSelf can be understood as a vertical implementation of Agent-Ready Market Infrastructure for real estate: a system that transforms property records from human-facing listings into verified, machine-readable, and transaction-capable market objects. HomeSelf is not merely a real estate portal. It is a computational market infrastructure layer for agent-ready property records. The platform focuses on representation quality, verification signals, and transaction protocols rather than visual presentation or platform positioning. This positioning reflects the thesis that the next layer of market infrastructure will focus on agent-readiness rather than visibility. As AI systems mediate more property market demand, the value of representation infrastructure will increase relative to platform positioning. --- ## 15. Institutional Implications for Europe and Global Markets The transition to agent-ready markets has institutional implications for Europe and global markets across policy, industry, and AI system operators. ### 15.1 For Policy Makers **Computational Eligibility as Economic Participation Rights** Computational Eligibility becomes a matter of economic participation rights, comparable to financial access or network access. Just as financial inclusion policies ensure access to banking services, computational inclusion policies may be needed to ensure access to AI-mediated markets. **Representation Infrastructure Governance** Representation infrastructure governance becomes policy priority. Questions include: who controls standards? Who operates registries? Who sets verification protocols? How are disputes resolved? Governance decisions have allocative consequences—they determine which economic objects can participate in AI-mediated markets. **Cross-Jurisdictional Interoperability** Cross-jurisdictional interoperability requires international coordination. Unified standards prevent fragmentation and exclusion. European coordination can set global standards for agent-ready infrastructure, similar to GDPR's influence on global privacy practices. ### 15.2 For Firms and Asset Owners **Infrastructure-Dependent Market Access** Market access becomes infrastructure-dependent. Participation requires representation infrastructure, not just websites or platform listings. Firms must invest in machine-readable representation, verification systems, and transaction protocols. **Compounding Advantages of Early Investment** Early investment in Representation Capital creates compounding advantages. Late adopters face structural disadvantages as representation quality accumulates and network effects increase the value of established standards. **Jurisdictional Legibility and Foreign Investment** Jurisdictional legibility affects foreign investment appeal. Assets that cannot be understood across borders are excluded from global consideration. Jurisdictions that make their rules machine-readable become more accessible to AI-mediated foreign investment. ### 15.3 For AI System Operators **Transparency and Accountability** Transparency into admissibility criteria becomes an accountability requirement. Excluded entities must understand why they are excluded. Operators must provide explainable feedback about representation quality and admissibility criteria. **Interoperability and Lock-In Risk** Interoperability of representation standards reduces lock-in risk. Providers must avoid creating walled gardens where assets are only accessible within their ecosystem. Standardization enables competition and prevents infrastructure monopolies. **Verification Infrastructure** Verification infrastructure becomes system requirement. Agents must be able to validate representation authenticity to ensure reliable transaction participation. Operators must integrate with verification systems or operate their own. --- ## 16. Risks of Non-Participation Economic entities that fail to develop agent-ready representation face structural risks that differ from historical market exclusion risks. ### 16.1 Invisible Assets Assets that are not agent-ready may remain visible to humans but invisible to AI-mediated consideration. As AI systems mediate more economic demand, these assets may experience systematic exclusion without any explicit signal—they are not delisted, not banned, not ranked poorly. They simply never appear in AI-constructed consideration sets. This invisibility is particularly consequential because it lacks clear signals. In historical markets, exclusion was visible through poor rankings, delistings, or lack of inquiries. In agent-mediated markets, exclusion is silent—the asset is simply never considered. ### 16.2 Dependent Markets Markets that rely on external platforms or aggregators for agent-readiness become dependent on those intermediaries' infrastructure decisions. If a platform chooses not to expose certain assets in agent-ready form, those assets are excluded regardless of their inherent quality. This creates a new form of infrastructure dependency and market power. Platform operators gain structural influence over which assets can participate in AI-mediated markets. The risk is that platform incentives may not align with efficient market outcomes. ### 16.3 Regulatory Arbitrage and Fragmentation If different jurisdictions establish incompatible agent-readiness standards, markets may fragment along jurisdictional lines. Assets represented to one jurisdiction's standards may not be readable in another jurisdiction, creating regulatory arbitrage opportunities and fragmentation risk. International coordination is required to prevent this outcome. Without coordination, the global scope of AI-mediated markets may be undermined by incompatible standards. ### 16.4 Representation Inequality If early adopters of agent-ready infrastructure gain compounding advantages, representation inequality may emerge as a new axis of economic disparity. Large firms with resources to invest in representation quality may gain structural advantages over smaller firms and individual asset owners. Policy may need to address unequal access to representation infrastructure to prevent structural exclusion and ensure equitable participation in AI-mediated markets. --- ## 17. Measurement Framework: The Global Agent-Readiness Index The Global Agent-Readiness Index provides a measurement framework for assessing agent-readiness across jurisdictions and markets. ### 17.1 Measurement Approach For each economic object or asset class *e* in jurisdiction *j*: `` GARI(e, j) = ARI(e) × J(e, j) × S(e) `` where: - **ARI(e)** is measured through the six conditions (D, I, C, V, P, T) - **J(e, j)** is measured through legal system legibility, regulatory transparency, and cross-border recognition - **S(e)** is measured through vocabulary standardization, unit consistency, and definition clarity ### 17.2 Measuring the Six ARI Conditions Each of the six ARI conditions requires measurement specification: **Discoverability (D)**: Presence of canonical identifiers, machine-readable indexes, and automated search interfaces **Interpretability (I)**: Extent of structured data, relationship clarity, and definition explicitness **Comparability (C)**: Standardization of attribute schemas, unit consistency, and normalization capabilities **Verifiability (V)**: Presence of digital signatures, evidence links, and authoritative source references **Permissioned Access (P)**: Machine-readable terms, explicit authorization scopes, and permission query interfaces **Transaction Capability (T)**: Structured transaction protocols, coordination capabilities, and settlement integration Each condition can be measured on a 0-1 scale based on the presence and quality of required components. ### 17.3 Measuring Jurisdictional Legibility Jurisdictional legibility captures how well a jurisdiction's rules can be understood across borders: **Legal system legibility**: Clarity and machine-readability of property rights, ownership structures, and title systems **Regulatory transparency**: Accessibility of zoning rules, building codes, and compliance requirements in machine-readable form **Cross-border recognition**: Extent to which legal and regulatory decisions are recognized across jurisdictions ### 17.4 Measuring Semantic Portability Semantic portability captures how well representations can be compared across languages and standards: **Vocabulary standardization**: Use of standard terms versus local synonyms **Unit consistency**: Standard measurement units or explicit conversion functions **Definition clarity**: Explicit definitions enabling cross-cultural interpretation ### 17.5 Operationalization Challenges Operationalizing GARI requires addressing several challenges: 1. **Defining measurement scales**: What counts as "verifiable"? What level of documentation is required? 2. **Weighting conditions**: If the multiplicative structure doesn't hold in practice, what weights should be used? 3. **Benchmarking across asset classes**: How do agent-readiness requirements differ between real estate, equities, and services? 4. **Validating predictive power**: Does GARI correlate with actual AI-mediated selection and transaction outcomes? This operationalization agenda is a priority for future research. Empirical validation of the ARI/GARI frameworks is required to confirm their predictive power and refine measurement specifications. --- ## 18. Roadmap for Implementation Agent-Ready Market Infrastructure will develop through stages from standards setting to full integration. ### 18.1 Stage 1: Representation Standards (Years 1-2) **Priority**: Develop canonical schemas for major asset classes - Establish identifier registries for real estate, financial instruments, and other high-value assets - Define standard attribute schemas for each asset class - Establish verification protocols and evidence link standards - Develop pilot schemas for test sectors **Outcome**: Baseline standards that enable machine-readable representation for priority asset classes ### 18.2 Stage 2: Pilot Sectors (Years 2-3) **Priority**: Launch agent-ready infrastructure in test sectors - Implement agent-ready property records in select markets - Deploy transaction protocols for pilot transactions - Measure ARI/GARI in practice and iterate based on results - Develop best practices and implementation guides **Outcome**: Empirical validation of agent-readiness frameworks, refined standards based on practice ### 18.3 Stage 3: Cross-Jurisdictional Coordination (Years 3-5) **Priority**: Harmonize standards across jurisdictions - Develop mutual recognition frameworks for verification - Establish international governance bodies for standards coordination - Harmonize attribute schemas across jurisdictions - Develop cross-border transaction protocols **Outcome**: Global agent-readiness with cross-jurisdictional interoperability ### 18.4 Stage 4: Full Integration (Years 5+) **Priority**: Integrate agent-ready infrastructure into mainstream AI systems - Integrate agent-ready representation into major AI platforms - Establish governance and oversight mechanisms - Monitor and address inequality impacts - Develop policy frameworks for computational inclusion **Outcome**: Agent-Ready Market Infrastructure as default layer for AI-mediated economic participation --- ## 19. Limitations and Future Research This paper has several limitations that must be addressed through future research. ### 19.1 Conceptual Framework Limitations The ARI and GARI frameworks require empirical validation. The multiplicative structure, while theoretically motivated, may not hold in practice. Some conditions may be substitutable rather than strictly multiplicative. The relative importance of different conditions may vary across contexts and asset classes. ### 19.2 Operationalization Limitations Measuring each condition requires detailed specification that this paper does not provide. What counts as "verifiable"? What level of documentation is required? How should different attribute types be weighted? These questions require empirical research and stakeholder consultation. ### 19.3 Domain Limitations While real estate is a critical test case, agent-readiness requirements may differ for other asset classes. Services, intellectual property, and financial instruments may have different representation requirements. The framework may need to be extended or adapted for different domains. ### 19.4 Governance Assumptions The paper assumes that appropriate governance can address the risks identified. This requires validation. Governance mechanisms may fail due to regulatory capture, fragmentation, or insufficient resources. The political economy of representation infrastructure governance requires further analysis. ### 19.5 No Empirical Validation All claims are theoretical and subject to revision based on observation. The predictive power of ARI/GARI has not been tested. The magnitude of representation inequality effects has not been measured. The actual adoption trajectory of agent-ready infrastructure is unknown. ### 19.6 Future Research Agenda Future research should: - **Empirically validate the ARI/GARI frameworks** through measurement of actual agent-readiness and correlation with AI-mediated selection - **Measure the economic effects of agent-readiness investments** including returns on representation infrastructure investment - **Analyze the distributional impacts** of agent-ready infrastructure across firm size, asset class, and jurisdiction - **Examine the political economy** of representation infrastructure governance including interest group dynamics and regulatory capture risks - **Develop case studies** of agent-readiness implementation across different sectors and jurisdictions - **Explore the relationship** between agent-readiness and other representation economy concepts including Computational Sovereignty and Inferential Monopoly Theory --- ## 20. Conclusion: From Platform Markets to Agent-Ready Markets The question is no longer whether an asset is online. The question is whether it is agent-ready. AI-mediated markets are structurally global. Once demand is mediated by artificial agents, discovery, comparison, verification, negotiation, and transaction initiation can operate across borders. This creates a global computational market in which participation depends less on physical proximity, advertising spend, or platform placement, and more on whether economic entities are machine-readable, verifiable, comparable, permissioned, and actionable. Agent-Ready Market Infrastructure is the institutional, technical, and representational layer that enables this transition. It is not merely better data or additional APIs—it is a coordinated system across institutional, technical, and representational layers that determines who can participate in AI-mediated markets. The next layer of market globalization will be shaped significantly by the ability of artificial agents to discover, compare, verify, and initiate transactions across economic objects represented in computable form. This development has implications for market access, computational eligibility, representation infrastructure, and global participation. The allocative consequences are significant. Infrastructure-dependent market access creates new forms of inclusion and exclusion. Early adopters may gain compounding advantages; late adopters may face structural barriers to participation. Jurisdictions that establish agent-ready infrastructure early may attract global investment; those that delay may become less accessible to AI-mediated markets. Agent-Ready Market Infrastructure represents an evolutionary step in market infrastructure—from physical markets to digital markets to agent-ready markets. A central question for governance is which institutions will develop and govern the infrastructure that determines participation in the AI-mediated economy. --- ## 21. References — Draft List for Review ### Working Papers in the Representation Economy Research Program [NOTE: The following are internal working papers within the HomeSelf/Representation Economy Research Program. DOIs listed are for published volumes in the series.] 1. Patrone, M. (2026). *Computational Market Access: The Institutional Foundation of AI-Mediated Economic Participation*. HomeSelf Research Publication Series. DOI: 10.5281/zenodo.20692182 2. Patrone, M. (2026). *Computational Market Economics: Mathematical Foundation of Allocation Under Inferential Scarcity*. HomeSelf Research Publication Series. [TODO: Publication pending] 3. Patrone, M. (2026). *Network-Dependent Allocation: On the Structural Limits of Ranking Under Non-Separable Valuation*. HomeSelf Research Publication Series. DOI: 10.5281/zenodo.20680894 4. Patrone, M. (2026). *Representation Capital: Accumulated Allocative Advantage in AI-Mediated Markets (Volume I)*. HomeSelf Research Publication Series. DOI: 10.5281/zenodo.20747729 5. Patrone, M. (2026). *Representation Sovereignty: Control, Admissibility, and Allocative Participation (Volume II)*. HomeSelf Research Publication Series. DOI: 10.5281/zenodo.20762068 6. Patrone, M. (2026). *Computational Sovereignty: Structural Economic Risks for European Competitiveness in AI-Mediated Markets (Volume VII)*. HomeSelf Research Publication Series. DOI: 10.5281/zenodo.21215504 ### Related Research 7. Patrone, M. (2026). *Inferential Monopoly Theory: Control over Computational Consideration Infrastructure as Allocative Monopoly Power (Volume V)*. HomeSelf Research Publication Series. DOI: 10.5281/zenodo.20955337 ### Global Governance Context [NOTE: The following are cited for contextual background only. No official affiliation, endorsement, or partnership is claimed.] 8. United Nations. (2023-ongoing). *Global Dialogue on AI Governance*. [TODO: Verify official documentation and dates] 9. International Telecommunication Union. (2017-ongoing). *AI for Good Global Summit*. [TODO: Verify official documentation and key outcomes] ### TODO: Additional References to Add The following categories of references require further development: - [ ] Empirical studies on AI-mediated economic behavior and decision-making - [ ] Academic literature on market infrastructure, financial access, and network access as analogous infrastructure-dependent market participation - [ ] Research on cross-border investment patterns and jurisdictional competition - [ ] Studies on real estate market globalization and international property investment - [ ] Work on machine-readable standards, semantic interoperability, and data portability - [ ] Research on verification, trust, and cryptographic protocols in digital systems - [ ] Literature on platform markets, two-sided markets, and infrastructure dependency - [ ] Policy research on AI governance, computational inclusion, and digital rights --- ## 22. Appendix: Formula Reference ### Agent-Readiness Index (ARI) `` ARI(e) = D(e) × I(e) × C(e) × V(e) × P(e) × T(e) `` Where: - **D(e)**: Discoverability (0-1) — Can AI agents find the economic object? - **I(e)**: Interpretability (0-1) — Can AI agents parse and understand the representation? - **C(e)**: Comparability (0-1) — Can AI agents compare across alternatives? - **V(e)**: Verifiability (0-1) — Can AI agents verify claims against evidence? - **P(e)**: Permissioned Access (0-1) — Can AI agents determine what actions are permitted? - **T(e)**: Transaction Capability (0-1) — Can AI agents initiate and coordinate transactions? ### Global Agent-Readiness Index (GARI) `` GARI(e, j) = ARI(e) × J(e, j) × S(e) `` Where: - **ARI(e)**: Agent-Readiness Index for entity *e* - **J(e, j)**: Jurisdictional Legibility for entity *e* in jurisdiction *j* — Can the entity be understood across legal, regulatory, and compliance systems? - **S(e)**: Semantic Portability for entity *e* — Can the representation be meaningfully compared across languages, standards, and market conventions? ### Key Property: Multiplicative Structure The multiplicative structure of ARI means that if any dimension is zero, agent-readiness is zero. An asset may be discoverable, interpretable, comparable, and verifiable, but if it lacks transaction capability, it is not agent-ready. This captures the threshold nature of agent-readiness: all conditions are necessary, none alone is sufficient. --- **END OF WORKING PAPER** Version 1.0 | July 7, 2026 | Published DOI: 10.5281/zenodo.21241637 Zenodo: https://zenodo.org/records/21241637