AI Agents for Customer Relations

Why AI Agents Are Game-Changers for Businesses

You might remember a Google keynote in May 2018 showcasing Google Duplex, an AI capable of performing real-world tasks over the phone. During this demonstration, Sundar Pichai, Google's CEO, asked the assistant to book an appointment at a hair salon.

The smooth and natural exchange impressed the audience and made headlines, promising a new era where AI would become an everyday assistant.

Yet, several years later, concrete use cases for Google Duplex remain rare and limited. While this demonstration captured imaginations, it didn't transform the landscape as one might have imagined. Technical issues and integration challenges slowed its widespread adoption.

Today, the concept of AI assistants has evolved to give rise to what are now called AI agents. These offer much more plausible use cases and promise to revolutionize entire sectors, especially customer relations. It's even becoming difficult to imagine an aspect of the customer experience that cannot be touched or improved by these AI agents.

Businesses that quickly grasp the potential of these systems gain a significant competitive advantage. Anyone looking to make their customer experience smoother, more efficient, and better adapted to expectations must absolutely look into these AI agents.

In this in-depth article, we will explore what these agents are, how they work step-by-step, their practical applications, and most importantly, how to integrate them today to sustainably improve your customer experience.

What are Artificial Intelligence Agents?

Understanding the Nature of AI Agents

Agents ofAI 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. This intelligent management allows them to accomplish complex tasks without human intervention. The underlyingAI models provide them with remarkable flexibility in information processing.

These systems represent a major evolution compared to simple chatbots or voice assistants. Their ability to learn from their interactions with users and adapt in real-time makes them particularly valuable for modern businesses.

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AI Agents and LLMs: A Complementary Relationship

AI agents are often confused with AI agents large language models (LLMs) like ChatGPT, Bard, or LLaMA. In reality, the two concepts are not competing but complementary.

To understand this better, it's helpful to distinguish between AI Agents and LLMs:

Large language models (LLM):

• These are systems trained on immense volumes of textual data.
• Their role is to understand and generate natural language: writing text, summarizing a document, answering a question.
• However, they lack true autonomy: they respond to a given instruction and then stop.
• They operate in a question-and-answer mode with no continuity between sessions
• Excellent at content generation and natural language understanding
• Require human intervention for each new task

The agents AI:

• They often use an LLM as a linguistic reasoning engine, but they go much further.
• Are capable of solving complex multi-step problems (such as creating a complete presentation on a specific topic), which involves sophisticated planning
• Have access to various tools to help them solve these complex problems (APIs, Google searches, code interpreters, vector databases, specialized frameworks...)
• Possess persistent memory, allowing them to recall different steps and learn from past actions
• Can interact with multiple applications simultaneously to accomplish cross-functional tasks
• Make autonomous decisions based on predefined rules and objectives

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The LLM is a fundamental building block, but theAI agent is the orchestrated application that leverages the LLM and other tools to perform concrete actions.

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Numerous use cases

AI agents stand out for their ability to combine continuous planning, execution, and learning. The LLM is a fundamental building block, but the AI agent is the orchestrated application that leverages the LLM and other tools to perform concrete actions. Here are some specific examples of tasks they can accomplish in various sectors:

In logistics management:

• Monitor stock levels in real-time and automatically place orders with suppliers based on predefined thresholds
• Optimize delivery routes by considering traffic, weather, and customer priorities
• Forecast future needs through predictive analysis of data historical

In human resources:

• Sort CVs based on multiple criteria, schedule interviews, and analyze candidate skills
• Automate new employee onboarding with personalized journeys
• Identify training needs by analyzing team performance

In customer service:

• Analyze the voice of the customer: Identify trends in customer reviews, detect recurring issues, and recommend improvements
• Manage complex complaints by accessing multiple systems simultaneously
• Personalize responses based on each customer's history and profile

In personal management:

• Manage personal tasks such as organizing schedules, booking tickets, and managing finances
• Coordinate family or professional events with all participants
• Optimize personal budgets by analyzing expenses and suggesting savings

A significant portion of tasks requiring automatic thought, decision-making, and action can be optimized, or even fully managed, by an AI agent. This capability radically transforms how businesses can serve their customers.

How AI Agents Work: A Detailed Step-by-Step Analysis

AI agents follow a structured multi-step process, from perceiving their environment to taking actions within it. This architecture allows them to operate with remarkable efficiency.

To facilitate understanding of this complex process, let's follow theexample of a restaurant that implemented an AI agent for automated email reservation management. This example perfectly illustrates how these systems transform repetitive tasks into intelligent processes.

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Step 1: Perception and Data Collection

The first fundamental step is to connect your data that will constitute the agent's environment (emails, phone calls, sensory sensors, web interface, third-party applications, etc.). This step is absolutely essential, as it determines what information will be stored, analyzed, and prioritized by the system.

The AI agent monitors the restaurant's inbox in real-time and analyzes each received email to detect reservation requests. Using models of advanced language, it identifies important information in the messages: customer name, desired date and time, number of people, special requests, dietary restrictions, etc.

"Hello, I would like to book a table for 4 people on Saturday, January 20th at 7 PM. It's for a birthday celebration, could you arrange a cake? We have a vegetarian person in our group. Thank you, Julien."

The agent automatically extracts the information key details:

• Name: Julien
• Date: January 20
• Time: 7 PM
• Number of people: 4
• Special request: Birthday cake
• Dietary restriction: 1 vegetarian

This extraction capability goes far beyond simple keyword searches. The agent understands context, the nuances of natural language, and can even interpret implicit requests.

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Step 2: Memory Flow and Intelligent Information Storage

The memory flow acts as an internal database for the agent. It doesn't just store information; it organizes, categorizes, and establishes connections between pieces of information. All data collected, including past decisions and actions, are recorded with precise timestamps and descriptive metadata.

The agent records reservation data in its memory system, including special requests. This allows it to:

• Instantly retrieve information relevant information from millions of data points
• Prioritize items based on their importance, recency, or criticality
• Learn patterns and improve performance over time
• Maintain consistency in its actions and decisions

In our example, the agent records the booking data in its system memory with all special requests. This memory allows it to:

• Check real-time availability in the restaurant's schedule
• Maintain a complete history of bookings for future reference
• Identify regular customers and their usual preferences
• Detect patterns (e.g., peak days, popular time slots, etc.)

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Step 3: Intelligent Retrieval and Contextual Analysis

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When it needs to make a decision, the agent doesn't just access its memory linearly. It uses sophisticated algorithms to extract the most relevant memories from its memory stream. This extraction is based on several criteria: data recency, contextual relevance, strategic importance, and connections with other information.

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This targeted extraction helps the agent to:

• Focus on the most useful information for the current situation
• Avoid information overload
• Make quick and relevant decisions
• Tailor actions to the specific context

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The agent checks the restaurant's schedule via the system to check availability. But it goes further:

• It analyzes the reservation history to anticipate potential no-shows
• It checks the restaurant's special events that evening
• It considers the average meal duration for this time slot
• It assesses the kitchen's capacity for special requests

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If a table is available, the agent prepares a personalized confirmation response. If no table is available, it intelligently searches for the closest alternative time slots, taking into account the usual preferences of similar customers.

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Step 4: Deep Reflection and Insight Generation

This step truly distinguishes AI agents from traditional automated systems. After analyzing the retrieved memories, the agent doesn't just apply predefined rules. It generates complex insights, identifies hidden implications, and formulates hypotheses.

These reflections are then reintegrated into the memory stream, creating a continuous learning cycle. The agent constantly improves its analytical and adaptive capabilities for future decisions. This learning process allows for constant evolution of the service.

In our example, the agent:

• Notes that Julien wants a birthday cake and understands the emotional significance of the event
• Anticipates additional needs (table decoration, special menu)
• Assesses the restaurant's ability to meet these special requests
• Considers the impact on other reservations and the organization of the service
• Suggests creative alternatives if certain requests cannot be met

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Step 5: Strategic Planning and Coordination

Planning represents the strategic core of the AI agent. Here, it formulates concrete actions based on analyzed data and generated insights. This step goes far beyond a simple automatic response: the agent develops a true action strategy.

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The agent automatically drafts a sophisticated email response, tailored to the tone and context. It can even adjust its style based on the customer's profile (new vs. regular, individual vs. business).

Example of a generated response if the table is available:

"Hello Julien,
We are delighted to confirm your reservation for 4 people on Saturday, January 20th at 7 PM. We've noted that this is for a birthday celebration – what a wonderful occasion!
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To make this moment even more special, we can indeed prepare a birthday cake. Our pastry chef offers several options: intense dark chocolate, classic strawberry cake, or lemon meringue tart. Could you please let us know your preference and the name of the person being celebrated?
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We also noted the presence of a vegetarian guest. Our chef has prepared a special vegetarian menu for that evening, which we would be delighted to present to you.
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Please don't hesitate to let us know if you have any other requests to make this evening memorable!
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Kind regards,
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The Restaurant Team"

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Step 6: Action, Reaction, and Continuous Adaptation

In this crucial final step, the agent implements planned actions while remaining vigilant to changes in its environment. This dual capacity for execution and adaptation distinguishes modern AI agents from traditional rigid systems.

In this crucial final step, the agent implements the planned actions while remaining vigilant to changes in its environment. This dual capacity for execution and adaptation distinguishes modern AI agents from traditional rigid systems.
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The agent:

• Sends the email to the client at the optimal time (neither too early, nor too late)
• Monitors responses and reacts accordingly
• Updates all relevant systems (scheduling, kitchen, service)
• Triggers parallel actions if necessary (ordering special ingredients, staff briefing)

If Julien responds with additional details, the agent automatically adjusts:
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• The reservation in the system
• The kitchen instructions
• The notes for the service team
• The automatic reminders before the date

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This real-time adaptability allows for a truly personalized and responsive customer experience, impossible to achieve with tools traditional ones.

How do AI agents transform the customer experience?

Faster, more efficient, and cheaper? Could improving customer experience be as simple as plugging in AI agents? The answer is nuanced but promising. Companies that master this technology are radically transforming their customer relationships.
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Data collection and management: the foundation of success

Now that you better understand how AI agents work, you can also see how each step can potentially be a source of problems, for example :

• Data collection: if your data is unreliable (lack of standardization in data formats, spam, duplicates, etc.), the analyses performed can be incorrect and lead to poor actions (delivery to the wrong address, incorrect recommendations...)‍
• Communication with tools: tools can fail or evolve without the agent being updated. Unlike a human who can adapt to an interface or process change, an AI agent requires a technicalreconfiguration.

Despite these challenges, the benefits of AI agents for customer experience are immense. Major brands are already leading the way by deploying these technologies to create more personalized and efficient experiences.

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Success stories: giants lead the way

Despite these challenges, the benefits of AI agents for the customer experience are immense and measurable. Major brands are already leading the way by deploying these technologies to create exceptional and differentiated customer experiences.

Deutsche Telekom offers an AI agent named askT, allowing employees to get information on internal policies and benefits, and plans to entrust it with tasks administrative.

Cosentino is deploying a "digital workforce" to manage customer service operations, effectively replacing several human roles and allowing staff to focus on other tasks.

H&M and Amazon use AI agents to help customers choose products tailored to their needs.

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Democratized Accessibility: AI Agents for All Businesses

Mark Zuckerberg recently stated that in the future, there will be "more AI agents than people", as businesses, creators, and individuals begin to design their own agents to accomplish various tasks . This vision is no longer science fiction but an emerging reality.

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“More AI agents than people” -Mark Zuckerberg

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This prediction is based on a rapid democratization of technology. More and more solutions, such as Microsoft Copilot, OpenAI agents, Google Vertex AI, or AWS Bedrock, are designed to be accessible to the general public and businesses of all sizes. Technical and financial barriers are rapidly falling.

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SMEs can now:

• Deploy a smart chatbot in a few hours without code
• Automate their service client 24/7 without a dedicated team
• Analyze customer sentiment in real-time
• Personalize customer journeys like the web giants

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Today, a small e-commerce site can implement a chatbot connected to multiple AI agents to manage complex tasks:

• Answer questions by understanding context and intent
• Process orders by accessing multiple systems
• Manage returns and complaints end-to-end
• Offer personalized AI-driven product recommendations
• Analyze customer reviews to identify areas for improvement

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With these tools, even a startup can offer a customer experience worthy of market leaders, without requiring a large team or massive investments.

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From customer data to concrete actions

To maximize the potential of AI agents, it is crucial to provide them with a reliable data environment. AI agents can only function effectively if the collected data is:

• Filtered: Eliminating spam, duplicates, or unusable data.
• Standardized: Ensuring information is organized and consistent.
• Analytically exploitable: Enabling analysis to generate relevant actions.

This preparation step is crucial for ensuring the successful integration of AI agents.
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Practical Solutions: The Glanceable Example

At Glanceable, we transform customer feedback into strategic decisions. Our proprietary AI unifies and analyzes data to reveal friction points and generate concrete actions.

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We perfectly illustrate this evolution with two complementary solutions:

• GIO, our AI assistant that acts as a true collaborator to instantly explore your data and uncover hidden insights
• Our AI agent workflows that automate processes complex, transforming days of manual work into intelligent automated actions

For example, what used to require 4-5 days of manual quality control transforms into instant detection with automatic triggering of corrective actions: exactly what modern AI agents promise.

Analytics tools combined with AI agents already enable:

• Generate targeted alerts as soon as a problem is detected
• Automate action plans following customer insights
• Orchestrate complex workflows between multiple systems
• Predict and prevent problems before they occur

AI Agents: The Future is Already Here

AI agents are no longer a futuristic promise but an operational reality. Unlike Google Duplex, they offer concrete use cases that are already transforming customer relations.

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Deploying AI Agents: How to Get Started?

To get started with AI agents:

1. Assess your current data and processes
2. Choose a high-impact pilot use case
3. Select the right partners (Microsoft Copilot, OpenAI, specialized solutions)
4. Train your teams in this new way of working
5. Continuously measure and iterate

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Businesses that delay adopting AI agents risk being quickly left behind. In a world where customers demand instant, personalized 24/7 responses, these systems are becoming essential.

Artificial intelligencesystems represent a major evolution for businesses. The ability of these machines to process information and make autonomous decisions is transforming how we serve our customers.

The evolution is clear: AI agents now act as true autonomous collaborators. They don't replacehumans but free up their potential for higher-value tasks.

So, why not consider testing an AI agent in your business today?

The future of customer relations is being written now with AI agents!