Property records for AI agents: Designing data for autonomous systems

AI agents need clear, machine-readable indicators of property identity and data provenance. Traditional records might include property addresses or internal IDs, but these are not sufficient for autonomous operations. Agents need canonical identifiers that persist across systems, source attribution indicating where data originates, and verification context showing who has confirmed the information. VPR provides this through protocol identity fields, owner attribution, and verification timestamps. When an AI agent queries a property record, it receives not just the data but also the context needed to assess reliability and provenance. This reduces ambiguity about what the property is, who owns it, and whether the information can be trusted.

For human operators, verification is often a separate process or implied through institutional trust. A property manager knows that data from their own systems is accurate without needing explicit verification indicators. AI agents cannot rely on such implicit trust - they need verification status as explicit, first-class data fields. VPR includes verification status, Trust Scores, and evidence links as required fields rather than optional metadata. An agent checking property availability receives not just availability information but also verification context indicating whether this availability is confirmed, inferred, or based on outdated information. This explicit verification context allows agents to make risk-aware decisions and to provide users with transparency about data reliability.

Property data has temporal dimensions that AI agents need to understand: when was this record last updated, how current is availability information, when does pricing expire? Traditional records may not expose this temporal context, leading agents to treat stale data as current. VPR includes timestamp fields for record creation, last update, and data-specific freshness where applicable. Availability timestamps indicate when availability was last confirmed. Pricing timestamps show when rates were set and whether they reflect current market conditions. This temporal context enables agents to assess data recency and to prioritize fresh information over cached or outdated records.

Property information is rarely universally valid-prices vary by season, occupancy restrictions apply during certain periods, and policies may differ for guest types. Human operators understand these contextual constraints intuitively, but AI agents need them made explicit. VPR structures constraints as machine-readable fields: seasonal pricing schedules, occupancy limits with conditions, policy variations by guest type, and availability windows with start and end dates. This explicit constraint representation allows agents to evaluate whether a property meets requirements for a specific query context rather than making incorrect assumptions about universal applicability.

Beyond static property information, AI agents need to understand what actions are possible and what state changes result from those actions. A booking agent needs to know not just current availability but how to initiate a booking, what information is required, and what state the property enters after booking. VPR structures action paths as metadata: booking initiation procedures, required documentation, state transitions, and cancellation terms. This action metadata enables agents to plan multi-step workflows, understanding the sequence of actions required to achieve a user goal. Without this structure, agents would need to infer action possibilities from unstructured terms and conditions, introducing error and friction.

AI agents need quantifiable signals of data quality to prioritize sources and filter unreliable information. Trust Scores, verification completeness metrics, and evidence density provide these signals in machine-readable form. A search agent might prioritize properties with higher Trust Scores when presenting options to users. A booking agent might require minimum verification thresholds before initiating transactions. VPR structures these trust signals as numerical fields that agents can filter, sort, and weight in decision-making. This quality-aware access enables agents to provide more reliable recommendations and to avoid operations based on unverified or incomplete data.

As more AI systems interact with property data, interoperability becomes critical. Different agents using different data formats create friction and require translation layers. VPR provides a standard ontology for agent-oriented property records, enabling interoperability across systems without custom translation. A property record published as VPR can be consumed by search agents, booking agents, evaluation agents, and reporting agents, each extracting the information relevant to their function. This standardization reduces integration overhead and enables agent ecosystems to scale without escalating complexity.

AI agents operating autonomously need structured error handling and recovery information. When booking fails, agents need to understand whether the failure is due to unavailability, policy violation, or system error. When verification fails, agents need to know what evidence is missing and how to proceed. VPR includes error codes and recovery paths as machine-readable metadata. Agents can match error types to predefined handling procedures rather than attempting ad-hoc interpretation. This structured error handling enables robust autonomous operations with predictable failure modes and clear recovery pathways.