Imagine a scenario: an AI assistant gets a prompt – “plan a four-day trip to Lisbon, under $1,500, two adults, mid-October.” Within seconds, it's searching, comparing, filtering. It knows the user prefers boutique hotels, dislikes layovers, and last time chose the option with free cancellation. It finds what looks like the right fit – a small hotel, good location, fair price. Then it tries to complete the booking.
And it fails. No endpoint to call, no API to authenticate against, no structured way in. The platform had availability and the right price. It just had no way to interact with the agent. So the agent moved on, and the platform never knew it had lost a booking.
This is the gap that a growing number of travel tech leaders are starting to name out loud. Not as a future risk to prepare for, but as something already happening. At Rebbix, we work closely with travel tech companies and talk regularly with the people building and running them. Lately, there’s been a pattern that comes up in those conversations often – the infrastructure most travel platforms are running on today wasn't built for AI travel assistants that are here to stay.
In this article, we look at what the shift to agentic AI demands from travel platforms at the technical level, and what getting agent-ready involves. If you're building or evolving a travel platform, this is the decision window that will determine whether your product stays visible as the booking layer changes underneath it.
The shift that's already underway
Our co-founder has recently launched his own podcast, Reframe First, where he talks to founders and executives about where the market is heading and what’s happening in their businesses. One of his recent guests, the founder & CEO of Tripian, voiced an emerging concern for travel tech businesses.
“AI agents are here to stay. They're real and they're highly effective. We see them being rolled out quite regularly, and I think that's not a trend. I think that is now going to be a part of real travel, and it's important that companies know how to address it and make themselves ready. One of the pain points that we're seeing in travel is that it has a lot of legacy infrastructure. AI agents need to consume API-first solutions, whether it's through MCP or other protocols. All that an agent is looking for is data, and it'll go to the place where it has data it can work with to complete a transaction or a booking. The problem is that a lot of these legacy companies aren't API-ready, so they are really trying to catch up to “how do we facilitate and work with agents.”
The numbers back that up. According to a November 2024 survey by Statista, 40% of global travelers already use AI tools for trip planning. Phocuswright puts the shift in even sharper terms: AI usage for trip planning among U.S. travelers jumped 11 points in a single year, and between a quarter and a third of travelers across the U.S. and Europe say they're already interested in letting an AI agent handle the booking itself, not just the research.
On the business side, 61% of travel companies surveyed by Phocuswright are already experimenting with or actively scaling agentic AI. So the direction is clear. What's less clear, for many platforms, is whether their infrastructure can support it.
What AI agents look for
To understand why many platforms are falling short, it helps to have a clear picture of how an AI agent works when it's planning a trip.
A human traveler opens a browser, scans a few pages, compares options visually, and makes a judgment call. An AI agent doesn't do any of that. It doesn't browse, doesn't skim. Instead, it queries – calls an endpoint, receives structured data, processes it, and decides what to do next. Agents do it all in seconds, across multiple systems simultaneously. If a platform can't respond to that query in a way the agent can parse and act on, the platform simply doesn't exist in that interaction.
This is the core of what "API-readiness" means in practice. A developer portal and a set of legacy integrations built for human-facing tools don't get you there. What agents require is something more specific: your inventory, pricing, availability, policies, and booking logic exposed through clean, authenticated, real-time endpoints that an autonomous system can call reliably and interpret correctly.
The emerging standard for this is MCP – Model Context Protocol – an open framework introduced by Anthropic in late 2024 that has since been adopted by OpenAI, Google DeepMind, and a growing number of travel companies. MCP sets a framework to structure information like rates, availability, and amenities so AI agents can see and understand the data. It’s a common language that lets an AI agent connect to a hotel's inventory system, an airline's availability feed, or a car rental platform without a custom-built integration for each.
In travel, where data and services are spread over many systems, vendors, and formats, each new integration has historically been slow and costly to build. MCP helps reduce this friction by providing a standard connection layer for exposing existing data and functionality to AI agents.
The platforms that are moving quickly understand this. Industry leaders like Kiwi.com and Sabre launched their own MCP servers in 2025, and major travel companies, including Booking.com, Expedia, and Turkish Airlines, are among those rapidly adopting the protocol. But for every company that has made this move, there are many more still running on static content, fragmented databases, and distribution logic that was never designed to be read by a machine acting autonomously.
The data problem is just as significant as the API problem, and the two are related. An agent calling an endpoint is only as useful as what that endpoint returns. If the data behind it is incomplete, inconsistent, or outdated, the agent either makes errors or skips the platform entirely. Inaccurate inventory, stale pricing, or inconsistent customer records become not just operational annoyances but sources of cascading errors when agents are booking, rebooking, and managing disruptions. In a human booking flow, a traveler might notice a discrepancy and ask a question. An agent won't. It will act on what it receives, or move on.
There's also a discoverability dimension that often gets overlooked. AI agents also need to understand what a platform offers in the first place. That means content needs to be structured and machine-readable, not optimized purely for human eyes or search engine rankings. A property description written to appeal emotionally to a traveler scanning a results page is not the same as a description an agent can interpret, categorize, and match to a user's stated preferences. It is no longer enough to rank well for keywords. Platforms must ensure that large language models and agents can accurately understand what they offer, when it is available, and why it is relevant to a specific traveler at any given moment.
Most travel platforms were built to serve a different kind of user entirely. Therefore, catching up means rethinking of how the platform makes itself available to the systems that are increasingly deciding where travelers go.
How to get agent-ready
For most travel platforms, becoming agent-ready is a sequence of decisions about infrastructure, data, and product design that build on each other. Some of these are technical in nature, some are architectural. But all of them affect whether an AI agent can find your platform, understand what you offer, and complete a transaction through you. Here's where to start.
1. Open up your APIs and make them reliable enough to be called autonomously
The most fundamental requirement is also the one where many travel platforms most often fall short. An AI agent doesn't navigate a UI. It calls an endpoint, receives a response, and acts on it. That means your pricing, availability, inventory, and booking logic need to be accessible through well-documented APIs that external systems can authenticate against and call consistently. Not just during business hours, not just for pre-approved partners, but reliably enough to support autonomous, real-time decision-making.
2. Make your content readable by machines, not just people
A well-written property description that appeals to a traveler scanning search results is not the same as content that an AI agent can work with. Agents don't read for tone or atmosphere. They parse for attributes: room type, cancellation policy, check-in window, included amenities, accessibility features, pet rules. If that information lives in unstructured CMS fields, buried in PDFs, or folded into marketing copy, an agent either misreads it or skips the listing entirely.
Structured data markup, for example, schema.org in JSON-LD format, is a practical starting point that gives agents and LLMs a consistent way to interpret what your platform offers and match it against a traveler's stated preferences. This applies not just to property content but to policies, pricing conditions, and any rules that affect whether a booking is viable for a specific user.
3. Build toward MCP compatibility
Model Context Protocol has moved quickly from an emerging standard to something approaching a baseline expectation among the platforms that are actively building for the agentic era. What it enables, at its core, is the ability for autonomous agents to reason, plan, and execute complex travel workflows securely and reliably, without requiring a bespoke integration for every AI system that wants to connect with your platform.
The practical implication for travel platforms is that publishing an MCP server, which exposes your inventory, availability, and booking capabilities in a format agents can discover and consume, is becoming the equivalent of what having a mobile-responsive website was a decade ago. A growing number of independent developers are already wrapping existing travel APIs into MCP-compatible interfaces, even when the original provider hasn't done so themselves. Platforms that build this capability directly, rather than waiting for third parties to do it for them, retain far more control over how their inventory is represented and transacted.
4. Treat data quality as infrastructure, not an afterthought
Most platforms have some version of a data quality problem: pricing that updates with a delay, availability that doesn't reflect recent changes, or property information that varies across distribution channels. In a human booking flow, these inconsistencies are an inconvenience. In an agentic one, they become operational failures.
The companies making meaningful progress on agentic AI are those treating data governance as a precondition rather than an afterthought by investing in data lineage, quality monitoring, and clear internal ownership before scaling any autonomous system. An agent that receives inaccurate data doesn't flag the discrepancy and move on. It acts on what it receives. That might mean completing a booking at a price that's no longer valid, or confirming availability that doesn't exist. The downstream consequences, including cancellations, refunds, and damaged trust, are significantly harder to recover from than the data problem that caused them.
A caching strategy that balances data freshness with API rate limits is a place to start. Real-time feeds for the data that changes frequently, like pricing, availability, or disruption status, and stable, well-maintained records for everything else.
5. Design for gradual autonomy from the beginning
According to Skift's State of Travel 2025 report, only 2% of travelers are currently willing to hand full control to an AI agent and let it book without any human oversight. That number will grow, but the point right now is that fully autonomous agent integrations assume a level of consumer trust that doesn't yet exist at scale.
The more durable approach is to design for levels of autonomy from the start. An agent might surface a recommendation and wait for approval before acting. Or it might complete a booking and immediately notify the traveler with full details. Or, for a user who has explicitly opted in, it might manage the entire itinerary end-to-end. These are different user experiences that require different technical architectures, different permissioning logic, and different approaches to confirmation.
However, building in the ability of your platform to interact with AI agents means your product can meet travelers where they currently are, and expand as their trust develops, without having to redesign the integration from scratch when the moment arrives.
The window is open – for now
The first wave of agentic commerce in travel is about visibility. This means the platforms that structure their data, open their APIs, and build toward interoperability now are the ones that will show up when an AI agent is deciding where to send a traveler. The ones that don't will simply be absent from that decision if a customer relies on AI travel assistants.
That's a different kind of competitive threat than travel tech has faced before. A platform that loses on price or user experience still gets seen. A platform that an agent can't read or interact with doesn't enter the conversation at all. As AI mediates more of the discovery and booking process, the gap between visible and invisible will widen faster than most teams currently expect.
At Rebbix, we've spent over a decade working inside travel tech – on the engineering side, close enough to the infrastructure to know that the decisions made at the platform level today determine what's actually buildable two years from now. Agent-readiness requires a fundamental change to how a platform makes itself available, including what it exposes, how it structures its data, and what systems it can talk to. The longer that change is deferred, the more of the product has to be unwound to accommodate it.
If you're not sure where your platform currently stands in terms of agent-readiness, whether your data is clean and consistent enough to support agent-led bookings, or whether MCP compatibility is on your roadmap or still an open question – that's exactly what we can help with. Get in touch with the Rebbix team, and let's take a look at what your platform needs to be ready for the future of travel.
.png)
.png)
.png)