AI agents in the hospitality industry

AI agents are at the forefront of digital transformation, fundamentally reshaping how businesses operate, automate processes, and interact with customers and systems. Whether in healthcare, finance, or hospitality, AI agents are poised to play a pivotal role.

So, AI agents do not just react to predefined commands, they work independently with minimal to no human supervision and accomplish goals, all while clearly demonstrating their reasoning and decision-making throughout the whole process.

They continuously adapt and learn over time by leveraging machine learning, natural language processing, and data analytics. Also by learning from previous interactions, they are able to respond to new or unfamiliar situations – even without prior training.

Their main value lies in their proactive nature, so the ability to execute complex tasks such as (in the context of the hospitality industry) reading an email request from a guest, carrying out their requested changes to their booking in the PMS, and emailing them back.

But what is the difference between an app and an AI agent?

Apps can be defined as software typically designed to perform a specific set of tasks, making their scope generally more rigid. They are not proactive, do not make decisions, and always require user input to function as predetermined.

Types of AI agents:

There are various types of AI agents existing, but mainly five are being distinguished: the reflex, the model-based, the goal-based, the utility-based, and the learning agents. Let’s break those down in the following:

Reflex agents

(Simple) reflex agents represent the most basic form of AI agents. They act solely based on current input, which means they don’t have any memory. They also do not communicate with other agents if information is missing. These agents operate according to predefined rules and are programmed to execute specific actions under specific conditions.

A good example of a (simple) reflex agent is a thermostat.

Model-based agents

Model-based agents, unlike (simple) reflex agents, use memory to evaluate potential outcomes and consequences before taking action or executing tasks. While their decisions are more informed, they are still constrained by an internal model of their environment.

Self-driving cars are an example of model-based agents. They use sensors to construct a model of their surroundings, based on which they can navigate autonomously.

Goal-based agents

Goal-based (or rule-based) agents are capable of planning their actions toward a defined goal or set of goals. By searching for action sequences that lead to a desired outcome, they can perform complex tasks while consistently working toward their overarching objective.

A good example of a goal-based agent is a chess-playing AI.

Utility-based agents

Utility-based agents go beyond simply achieving a goal. They use advanced reasoning algorithms to evaluate and compare different scenarios, selecting the action that provides the highest overall utility or benefit to the user. For these agents, the quality of the solution and the value delivered to the user are of high importance.

An example of a utility-based agent would be dynamic pricing systems, such as those used in hotels. The price for a stay can vary depending on factors like regional holidays or high-demand periods.

Learning agents

Learning agents can be either utility-based or goal-based, but what sets them apart is their ability to learn from new situations and feedback. They continuously expand their knowledge base, enabling them to adapt to unfamiliar environments and improve performance over time.

A good example of a learning agent is ChatGPT, or recommendation systems used by services like Netflix or online retailers.

How do AI agents work?

AI agents are characterised by their proactive nature, often operating autonomously when executing tasks. At the core of these agents are large language models (LLMs), which enable them to plan actions and develop memory based on past experiences and interactions. This memory helps optimise workflows over time.

Additionally, by interacting with various tools, systems, and APIs - a process known as tool calling- AI agents can keep their knowledge up to date, allowing them to handle complex tasks with greater efficiency and precision.

There are three steps that describe how AI agents function:

Perception and data collection: AI agents begin by gathering information from their environment through sensors, APIs, or other input sources. This data is essential for contextual understanding and is processed in real time, enabling the agent to remain up to date and perform tasks efficiently.
Processing: Using machine learning models, AI agents analyse and interpret the collected data. They identify patterns, make data-driven decisions, and continuously learn from new input and experiences. Over time, this ongoing learning process refines their algorithms, improving the accuracy and effectiveness of their responses.
Action: Once a decision is made, AI agents interact with their environment to carry out the required task - this may even follow a goal-based approach. Their actions are optimised for efficiency, ensuring that desired outcomes are delivered as effectively as possible.

Benefits of AI agents:

Operational efficiency

AI agents can automate manual, repetitive tasks, leading to increased efficiency and fewer human-caused errors. As a result, human intervention becomes less necessary - or even obsolete - allowing teams to focus on more complex, value-adding work.

Enhanced customer experience

AI agents are available 24/7 and capable of handling multiple tasks simultaneously. This ensures consistently high response quality and faster resolution times, ultimately improving the customer experience.

As AI agents continue to learn and evolve, their responses become increasingly tailored and personalised, further enhancing the quality of interactions.

Data-driven insights & better decision-making

By processing and analysing large volumes of data, AI agents uncover valuable insights that go beyond simple process optimisation. They help identify trends, understand customer behaviour, and support more informed, data-driven decision-making.

Flexibility & scalability

AI agents continuously learn from data, enabling them to adapt to new requirements, unexpected challenges, or unfamiliar scenarios. They are highly scalable and can manage growing volumes of interactions without compromising service quality.

Furthermore, integration with existing systems (e.g., a PMS) is straightforward and cost-effective - making AI agents a flexible and sustainable solution.

AI agents in the hospitality industry

As of early 2025, the development of AI agents has entered a transitional phase. These systems are already functional in certain controlled environments, but they are not yet fully applicable across all scenarios in various industries. A clear gap remains between prototype agents and fully production-ready solutions. Current challenges include the coordination of multiple agents and the handling of critical scenarios where human oversight is still required.

Despite these limitations, there is a significant and growing interest in the potential of AI agents, underscoring their future strategic importance for industries like hospitality.

In the hospitality industry, AI agents offer a wide range of promising opportunities. They can significantly enhance the guest experience while streamlining and automating operational processes. Additionally, they can also support more data-driven decision-making, which enables hoteliers to respond more swiftly and effectively to market demands.

To provide a concrete example of how AI agents can be used in the hospitality industry: by analyzing market demands and competitor pricing, they can dynamically adjust room rates, the goal thereby being to maximze revenue and the occupancy rate.

Here’s what Uli, our CEO and one of our founders, thinks about the future of AI agents in the hospitality industry:

AI agents will forever change the way people plan and book their travel. This could sideline those hotels that have traditionally been slow to modernize their tech stacks while accelerating the success of those that invest in the foundations today. OTAs can sink as much money as they want into their own AI projects, but – make no mistake – hotels that play their cards right can use AI agents as a golden ticket to bypass the middlemen and connect directly with guests.

Interested in the full article? You can read it here: AI agents will forever change the way people plan and book their travel.

So, what do AI agents actually mean for your hotel’s tech stack? In short: fewer limitations and greater flexibility

Most hoteliers nowadays rely on a PMS along with systems like a CRM or RMS, which can give the appearance of a well-integrated setup. However, significant challenges still exist, such as the different limitations of each system and business logic often tied to static rules or siloed applications.

With AI and AI agents in the hospitality industry, the PMS will evolve from being the central system to becoming a center of records, primarily focused on financial transactions, payments, compliance, and reporting. Similarly, systems like CRM or CDP will no longer merely connect to the PMS; each will serve as its own center of record, managing aspects such as guest profiles and behaviour.

The integration of AI agents is thereby not the beginning of a revolution, but rather a natural evolution in a long-standing journey, from traditional on-premise PMS solutions, through the cloud era, to the rise of open APIs and no-code/low-code.

As AI agents can access open databases and APIs, they will serve as the business logic, dynamically interacting with all types of systems, executing tasks, making decisions, and generating new, valuable insights. With the use of AI agents, who will be the business logic, powerful integrations get accessible to all and the need for technical expertise becomes ever more obsolete.

Model Context Protocol (MCP) & the Agent Hub

Integrating AI agents with a PMS or other systems is often a complex, time-consuming process that typically requires custom-built solutions. The Model Context Protocol (MCP) addresses this challenge by providing a standardised interface that simplifies the integration of AI agents, AI apps, and AI functionalities.

At its core, MCP defines a uniform way for AI agents to connect with integrations and external data sources. It standardises how agents access, retrieve, and interact with external applications like a PMS, making the integration process faster and more scalable.

Our Agent Hub is a key component of the Apaleo ecosystem and is the first marketplace and collaboration space for AI agents in the hospitality industry. By leveraging MCP, it strengthens our tech infrastructure and fosters an open environment that supports AI-driven developments, including AI agents and AI apps.

The Agent Hub enables hoteliers and developers to discover, implement, and share customised solutions that enhance guest experiences, streamline operations, and support scalable growth. These agents function like on-demand digital staff, executing tasks efficiently.

In the Agent Hub, you’ll find low-code/no-code agents and AI apps. Low-code/no-code agents are fully readable, allowing users to download their source code, customise it, and deploy it independently. AI apps, on the other hand, operate without exposing their inner workings and logic. They integrate easily with Apaleo via the Agent Hub with a simple “click and connect” process.

Want to check out our Agent Hub? Click here.

Have you built an AI agent that could be a great fit for the Agent Hub? Click here to share it with us. If it’s an AI app, please ensure that it’s built using an Authorization Code Flow.

Recommendations & outlook

AI agents are reshaping the digital landscape across industries. By understanding their capabilities and implementing them strategically, hoteliers can unlock new levels of efficiency, personalisation, and innovation. So, are you ready to start your AI journey today?

Here are a few recommendations to get you started:

Invest in an open, flexible infrastructure: Ensure that your PMS, CRM, and other core systems expose their functionality and data through APIs. This will prepare you to adopt AI agents when the time comes.
Adopt a mindset of experimentation: Innovation cycles are shorter than ever, and the barriers to trying new technologies are lower. Start experimenting - you’ll see results faster than you expect.
Prepare for a shift in business logic: Familiarise yourself with how AI agents work and explore areas where they can create immediate impact - whether it’s automating repetitive tasks or optimising workflows.

This shift will only accelerate, demanding a revamp of traditional systems and paving the way for agile, agent-driven platforms that automatically keep pace with the long march of technology.

You can read the full Hotel Yearbook article here: Top 10 trends to drive your hospitality into an agent-first world.