You may recall a Google keynote from May 2018 showcasing Google Duplex, an AI assistant capable of performing real-world tasks over the phone. During this demonstration, Sundar Pichai, CEO of Google, asked the assistant to book a hair salon appointment.
The smooth and natural exchange impressed the audience and made headlines, promising a new era where AI would become an everyday assistant.
However, several years later, concrete use cases for Google Duplex remain rare and limited. While the demonstration left a lasting impression, it did not transform the landscape as one might have expected.
Today, the concept of AI assistants has evolved into what we now call AI agents. These agents offer far more plausible use cases and promise to revolutionize entire sectors, particularly customer relations. It is becoming difficult to imagine any aspect of the customer experience that cannot be touched or improved by AI agents.
Anyone looking to make their customer experience smoother, more efficient, and better tailored to expectations should seriously consider AI agents.
In this article, we will explore what AI agents are, how they work, and, most importantly, how to integrate them today to sustainably enhance your customer experience.
AI agents are autonomous systems that leverage artificial intelligence to interpret their environment, make decisions, and perform actions to achieve specific goals.
They combine perception, reasoning, and execution capabilities to solve problems autonomously and efficiently.
To fully understand, it's helpful to distinguish between AI Agents and LLM Prompts:
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AI agents stand out for their ability to combine planning, execution, and continuous learning. Here are some concrete examples of what they can accomplish:
Many tasks that require thinking, decision-making, and automatic action can be optimized—or even entirely managed—by an AI agent. To fully measure their potential, it is essential to understand how they work and how to integrate them into your processes. That's what we'll explore next.
AI agents generally follow the same steps, from perceiving their environment to acting upon it.
To make this easier to understand, let's follow the example of a restaurant that has implemented an AI agent to manage reservations via email.
The first step is to connect your data, which forms the agent's environment (emails, phone calls, sensors, web interfaces, etc.). This step is crucial as it determines which information will be stored and prioritized.
The AI agent monitors the restaurant's email inbox in real time and analyzes each received email to detect reservation requests. It identifies important information in the messages: Customer name, desired date and time, special requests…
"Hello, I would like to book a table for 4 people on Saturday, January 20th, at 7:00 PM. It's to celebrate a birthday—could you arrange for a cake? Thank you, Julien."
The agent automatically extracts key information:
The memory flow acts as the agent's internal database. It stores and organizes all collected data, including past decisions and actions, with timestamps and descriptions. This allows the agent to quickly retrieve the most relevant information and prioritize recent or critical data.
The agent records the reservation data in its memory system, including special requests. This allows it to:
When the agent needs to make a decision, it retrieves relevant memories from its memory flow based on their recency, relevance, and importance. This targeted retrieval helps the agent focus on the most useful information to guide its actions.
The agent consults the restaurant’s scheduling system (table management system) to check if a table is available on January 20th at 7:00 PM for 4 people.
After analyzing the retrieved information, the agent generates complex insights and implications. These reflections are then reintegrated into the memory flow, allowing the agent to improve its learning and adaptability for future decisions.
The agent takes special requests into account to formulate a tailored response:
Here, the agent formulates actions based on the analyzed data and generated insights. The decisions made are also stored in memory to ensure consistency and inform future actions. Careful planning helps the agent act precisely and strategically.
The agent automatically drafts a response email based on the previous information and analysis.
For example, if the table is available:
"Hello Julien, your reservation for 4 people on Saturday, January 20th, at 7:00 PM is confirmed. We have noted that you would like a birthday cake. Could you please specify the flavor and the number of servings? Thank you for your trust!"
In this final step, the agent implements planned actions or reacts to new data if unexpected changes occur in its environment. This dual capability allows the agent to execute predefined strategies while remaining flexible in the face of new challenges.
The agent sends the email to the client and looks out for the response. If Julien accepts the reservation or requests a modification, the agent automatically adjusts the booking and updates the schedule.
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As AI agents become more adept at reasoning, planning, and self-monitoring, they will be able to handle tasks that assist users, such as specialized coding, or manage more tedious tasks quickly and at scale.
Although these technologies are still in their early stages, their potential is immense. Few companies today fully grasp the range of possibilities offered by AI agents, and even fewer have conducted concrete tests. Unlike Google Assistant presented six years ago, AI agents now have real-world use cases that are already transforming customer relations in major brands.
We hope this article has made their functioning clearer for you and sparked new ideas. So, why not consider testing an AI agent in your business today?
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