From booking engines to boarding gates, artificial intelligence is reshaping how we travel. Today, more than half of travel and hospitality companies already use AI-powered digital assistants to guide customers through booking, check-in, and even in-trip support. But a new wave of automation is on the horizon — one that’s far more independent. Enter agentic AI: autonomous, goal-driven systems capable of performing complex tasks with minimal human oversight.

It’s the next big leap in travel technology — but for it to work, the industry’s APIs need to evolve.

Where Agentic AI Meets the Travel API Ecosystem

If you’ve ever booked a flight through Expedia, a hotel on Booking.com, or a cruise via your travel agent’s platform, you’ve already benefited from the invisible web of travel APIs that keep this ecosystem running. These APIs connect airlines, hotels, tour operators, and booking platforms in real time — distributing inventory and pricing information across the industry.

Global distribution systems (GDS) like Sabre, Amadeus, and Travelport sit at the core of this system, handling millions of transactions every day. They were built to serve enterprise software and human developers, not autonomous AI agents capable of taking action on their own.

Sabre’s APIs, for example, operate within deeply structured, contract-based frameworks. Access often requires manual onboarding, credential setup, authentication, and business agreements. For humans, that’s just procedure. For autonomous agents, it’s a roadblock. Agentic AI thrives on speed, adaptability, and instant access—qualities not exactly synonymous with traditional API onboarding.

The Barriers to Agentic AI Integration

Let’s start with the obvious: agentic AI can’t sign contracts or request API access credentials. Every step—from creating an account to getting approved — involves human oversight. That makes spontaneous integration impossible unless credentials are pre-authorized, which defeats much of the promise of autonomy.

Then there’s rate limiting. Sabre and other GDS platforms typically cap the number of API calls a client can make within a given timeframe. This ensures system stability, but it also throttles how freely an AI agent can operate. If an agent runs rapid, large-scale searches or price comparisons, it can easily hit unseen limits — and fail silently.

Parsing large and complex responses is another technical challenge. Availability searches across hundreds of flights or hotel options generate huge JSON or XML payloads that even sophisticated large language models (LLMs) struggle to interpret efficiently. Without structured formatting or preprocessing, the AI might “hallucinate” — confidently guessing the wrong results, which is exactly what no one wants when booking a €2,000 business trip.

And then, of course, there’s compliance. Commercial travel APIs handle highly sensitive personal and payment data, so they must comply with PCI-DSS, GDPR, and other privacy frameworks. GDS systems are already compliant — but what about the AI agents that interact with them? These agents must never retain or process personally identifiable information (PII) beyond what’s strictly necessary. Given that many AI models aren’t built for data minimization or “forgetfulness,” compliance becomes a moving target.

Making APIs Agent-Ready

So how can the travel industry prepare for this next phase of AI-driven commerce?

The short answer: by rethinking how APIs are built and accessed.

Sabre’s Model Context Protocol (MCP) is an early attempt to bridge this gap. The MCP acts as an intermediary layer between AI agents and real-world systems, allowing the agent to execute tasks like “searchFlights” or “bookHotel” without manually handling every API call. Think of it as a translation layer — one that converts a natural-language command into structured, compliant API operations.

It’s currently invite-only, but developers can already build their own MCP servers to manage API connections safely and efficiently. If adopted more widely, MCP-style frameworks could become the standard layer that lets agentic AI operate across different travel systems.

Another key improvement involves optimizing data delivery. APIs must be structured to provide responses that are concise, consistent, and easy for AI to parse. Techniques like result caching, streamlined query responses, and simplified schemas can drastically reduce processing errors and improve real-time performance.

And on the compliance front, intermediary servers or “filters” should ensure that no PII ever reaches the AI model directly. The AI only receives the contextual data it needs to make a decision—nothing more, nothing less.

The Bigger Picture: How the Industry Is Moving

Other sectors are already ahead of the travel industry here. In fintech, for example, open banking APIs have evolved rapidly to accommodate machine-driven systems while preserving strict compliance. Platforms like Plaid and TrueLayer have shown that with smart API design and standardized access layers, autonomous or semi-autonomous agents can operate safely and at scale.

In travel, players like Amadeus are experimenting with similar ideas, offering AI-powered interfaces for agents and developers through their Amadeus for Developers platform. But the true shift will happen when APIs are designed from the start to support agentic AI — not just patched for compatibility later.

Because agentic AI doesn’t just automate. It decides. It negotiates, compares, and books — actions that demand both freedom and responsibility.

Conclusion: The Road Ahead for AI-Driven Travel

Travel has always been about connection—between people, destinations, and ideas. But as AI becomes a full-fledged participant in this ecosystem, those connections now depend on something less visible yet more critical: the APIs that make autonomous actions possible.

The rise of agentic AI isn’t just a tech upgrade; it’s a shift in the very architecture of travel commerce. The industry that once digitized paper tickets and automated bookings must now prepare for intelligent systems that act on intent, not just instruction.

Sabre’s MCP experiment, Amadeus’s AI initiatives, and the broader move toward agent-friendly APIs point to a clear direction: the travel industry is entering an era where AI doesn’t just assist—it operates. And to keep up, the infrastructure behind it must be as smart, flexible, and autonomous as the agents themselves.

The winners in this next phase won’t just be the companies with the best data or biggest inventories. They’ll be the ones who redesign the rails—the APIs—to power the next generation of intelligent, connected travel.