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Agent-to-agent (A2A) interaction means separately operated software agents communicate and coordinate tasks. In travel, one agent could represent a traveler or booking intermediary while another represents an airline, hotel, or other supplier. That could help systems work across organizational boundaries—but communication alone does not authorize a booking, payment, or ticket issuance.

What does A2A mean in a travel context?

Microsoft Learn describes A2A as an open protocol that lets agents discover one another, exchange messages, and coordinate tasks across services, teams, and organizations. In a booking scenario, a traveler-facing agent might ask a supplier-side agent about availability and terms, then pass a request or response between the two systems. Microsoft Learn’s A2A guidance distinguishes this kind of cross-boundary coordination from a simpler in-process tool call, which may be sufficient when agents operate inside one application and under one team.

A2A describes how agents communicate; it does not by itself establish what an agent may do. A message that requests a reservation is not proof that the traveler approved it, that payment is authorized, or that a supplier completed ticketing.

What could agents coordinate during a trip?

A possible workflow starts with a traveler-facing or intermediary agent collecting constraints such as dates, route, and preferences. It could query supplier agents, compare the options returned, and request the traveler’s confirmation before asking a supplier to act. After a reservation, agents might exchange status or coordinate a response to a disruption or cancellation.

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For this to work reliably, supplier agents would need to return structured, current availability and terms, along with clear results for requested actions. The systems would also need ways to handle incomplete responses, changed inventory, failed connections, and inconsistent versions. These are design requirements for a cross-organization workflow, not evidence that general-purpose consumer booking agents already support it.

What does the IATA Project Carina example demonstrate?

The clearest travel-sector example in the available sources is IATA’s 2026 Data and Technology proof of concept, Project Carina. It explores coordination for cargo interline operations among four fictitious airlines, using mock Cargo Management System and agreement data. Its workflows are labeled “Initial Booking,” “Disruption Management,” and “Booking Cancellation.”

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This is a demonstration of interoperability patterns for cargo operations—not a production integration and not a consumer passenger-booking service. It shows how agents might coordinate analogous tasks across carrier systems, including feasibility, partner availability, disruption handling, and cancellation. It does not establish that travelers can currently delegate live passenger bookings to arbitrary airline or hotel agents.

IATA frames AI in this setting as decision support for cargo operations. Its paper describes human-in-the-loop approvals, explicit decision states, explainability, auditability, and zero-trust controls. Those controls matter because a useful agent workflow must make clear what was proposed, approved, and actually carried out, and preserve a record of decisions.

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What must be in place before an agent can book?

Communication is only one part of a safe transaction. Before an agent can make a change or purchase, the parties need to establish both identity and authority: which agent is acting, whom it represents, which supplier or merchant it is addressing, and what action the user approved.

Visa’s Trusted Agent Protocol overview describes signed agent identity and authorization tied to a specific operation and merchant domain. Visa says the protocol is in development and deployment; it should not be read as a universally available travel-booking mechanism. The distinction is practical: permission to search or compare options does not necessarily include permission to reserve, change, cancel, or pay.

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Travel services would also need to return intelligible action results. A successful message exchange is not the same as a confirmed reservation. The workflow should distinguish a proposal, a pending approval, a supplier acceptance, and a completed transaction so a traveler or human operator can see what actually happened.

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How to assess an A2A travel workflow

Whether evaluating a concept or a deployed service, these dimensions help separate a communication demo from a trustworthy booking capability:

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  • Boundary: Is the interaction within one application, between internal services, or across separate organizations? Remote A2A adds network latency and distributed-system concerns such as timeouts, retries, failures, and versioning.
  • Workflow coverage: Does it only discover options, or does it also handle booking, changes, disruptions, cancellation, and post-booking support?
  • Maturity: Is the system a design, a mock-data proof of concept, a limited pilot, or a production service? Project Carina is a mock-data proof of concept.
  • Trust and authorization: Can the parties verify agent identity, the user represented, the merchant or supplier addressed, and whether consent covers this exact action?
  • Human control and accountability: Are approval points, decision history, explanations, and recovery procedures clear when an agent or connection fails?

Interoperability standards are also developing. ITU-T lists Recommendation F.748.93, “Framework and Requirements for AI Agent Interoperability”, as in force (prepublished), with the recommendation dated 08/26. It is a general agent-interoperability framework, not evidence that travel suppliers uniformly use the recommendation.

What travelers should expect today

A2A offers a way for independently operated agents to coordinate across systems, potentially extending beyond the initial search to operational tasks such as disruption handling and cancellation. The cited travel example, however, is a simulated cargo interline proof of concept. The available sources do not establish a consumer service that lets travelers hand off live passenger bookings to arbitrary supplier agents, or quantify a measured booking benefit from doing so.

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