60% is the percentage of companies (Thunderbit, 2025) that have deployed automation systems in their professional environments.

75% is the percentage of companies worldwide that have automated their sales. Among these companies, 61% use automation in business-to-business (B2B) operations.

According to a Thunderbit study published in 2025, the global market for industrial automation and control systems was worth approximately $206 billion US dollar in 2024.

By 2025, the study predicted a result of approximately $226.8 billion US dollars.

Regarding the distribution of automation in services, the Vena Solutions study (2025) shows a reduction in working time in various services:

• Finance sector:

RPA (Robotic Process Automation) and AI systems have replaced up to 80% of transactional accounting tasks that can be automated. Today, automation in payments allows 500 hours to be reallocated to other tasks.

• Human resource sectors:

By 2024, automation in the areas of hiring and payroll will have increased by nearly 600%.

• Marketing sectors:

58% of marketing managers have automated their social media posts.

Approximately two out of three marketing managers (58%) have automated their email campaigns. And one in three marketing managers (33%) have automated the content of their posts.

• IT sector:

For 90% of employees, with increased productivity, teamwork has improved thanks to the deployment of automation in information systems departments.

Agentic AI: Overview and Definition

AI, Generative AI, Agent AI, Agentic AI—while the term AI (artificial intelligence) is common to all these expressions, their specifications and uses have nothing in common. Each expression defines a tool and a very specific use.

AI is an automatic system that is capable of executing predefined and logical rules.

Generative AI is an AI system that is capable of generating text, images, sound (music, speech, etc.) or videos.

AI agents, explained very concisely, are semi-automated systems that require human intervention to function. The Muse™ editorial team has previously covered this topic. We invite you to (re)read our article in the Introducing section: AI Agents.

The main differences between AI agents and agentic AI are that agentic AI has autonomy in how it operates.

Agentic AI also has the ability to learn, adapt, and make decisions when encountering new situations.

To guide you through this article, you will find a reading plan below.

After discussing the differences between AI agents and agentic AI, we will take a detailed look at these characteristics and how they work.

We will then examine how they function by studying their working environment and work processes.

Next, we will look at the different configurations that are possible with agentic AI.

Finally, we will address the most complex part to understand, which is the details of each layer of the architecture.

Agentic AI: Characteristics

To begin with, Agentic AI can be considered a variant of AI Agent. This can cause some confusion in explanations.

As mentioned in the previous paragraph, what differentiates Agentic AI from Agent AI is its learning capabilities. But that’s not all!

Agentic AI is programmed to have several features in addition to its learning capabilities.

Unlike an AI system, agentic AI is capable of acting autonomously or semi-autonomously in interaction with the real world in order to achieve a goal. It is at this point that distinguishes an AI agent from agentic AI.

Agentic AI has four characteristics: adaptability, autonomy and decision-making, interoperability and language comprehension.

• Adaptability:

Agentic AI is able to learn as it encounters new situations.

• Autonomy & Decision-making:

Agentic AI is able to execute processes and tasks without human intervention.

Agentic AI is able to make decisions when choices are available to it. Humans do not interact with Agentic AI.

• Interoperability:

Agentic AI is able to connect to different data sources (search engines, databases, etc.) to generate better results. Agentic AI is also able to connect to different tools to increase its interaction with its environment.

• Language Comprehension:

Agentic AI offers an understanding of instructions given in natural language.

Agentic AI optimizes workflows, reducing financial costs and improving performance in production or in areas requiring quick and thoughtful decision-making.

These features bring a range of improvements, optimizations, and autonomy to various sectors, from finance to industry.

In concrete terms, this leads to improvements in embedded systems for autonomous cars and inventory optimization in warehouse management. It also enables the automation of restaurant reservations and hotel room bookings. It even makes it possible to manage orders for non-manufactured products or raw materials.

Agentic AI Frameworks

After discussing the characteristics of agentic AI, we will now look at agentic AI frameworks. We are starting with frameworks because they provide a better understanding of the possibilities that agentic AI can offer you. They also provide a better understanding of the more technical aspects of agentic AI.

Agentic AI frameworks enable the definition of specific operating structures and guidelines for agentic AI systems. The term “architecture” is also used instead of the framework.

In short, each framework (or architecture) enables a specific type of agentic AI to work efficiently. Applying the right framework allows agentic AI to function optimally in its tasks in order to better meet your future needs.

We can define three main frameworks (or architectures). These three frameworks (or architectures) can be reactive, deliberative, and cognitive.

• Reactive Architectures

This is an agentic AI system with a simple architecture. Simple in the way that Agentic AIs interact. These Agentic AIs act without accessing their internal memories and without planning their actions.

• Deliberative Architectures

This is an Agentic AI system with an architecture capable of reasoning. These Agentic AIs are capable of reasoning about a task to be performed and making decisions.

• Cognitive Architectures

This is an Agentic AI system with an architecture capable of simulating the cognitive abilities of human learning.

This architecture combines several elements present in other frameworks. These combined elements are the ability to perceive, reason, learn, and make decisions.

Agentic AI Workflows

In the previous paragraph, we discussed the frameworks for agentic AI. Now we will discuss the process of how each agentic AI works.

Regardless of their final assignments, all agentic AIs use the same workflow process.

This workflow is divided into eight steps. The eight steps are: Goal Interpretation, Task Planning, Tool Selection, Segmentation, Interpretation, Reinforcement, Feedback Loop, and Completion. The eighth step, Completion, corresponds to a return to the initial state (step 1: Goal Interpretation).

• Step 1: Goal Interpretation

Interpretation and structuring of data by the system by translating natural language into machine language.

• Step 2: Task Planning

After understanding and structuring the data, the agentic AI breaks it down into several sequences based on the context.

• Step 3: Tool Selection

After sequencing the tasks, the agentic AI selects one or more tools based on requirements. The agentic AI uses API links to establish connections with the tools.

• Step 4: Segmentation

To better manage the workload and system resources, each task is distributed evenly.

• Step 5: Interpretation

The system checks each segment. Throughout the process, the system verifies the progress and consistency of the result obtained with what was requested.

• Step 6: Reinforcement and Reflection

In this step, the system validates the successful results, confirming that they comply with what was requested (Reinforcement). On the other hand, in the event of a poor result, the system analyzes and corrects the non-compliant results (Reflection). This correction is carried out either during the process or at the end of each session.

• Step 7: Feedback Loop

The feedback loop step allows the Agentic AI to improve the results of its processes. During this step, the agentic AI adjusts its behavior to improve its results without modifying its source code.

• Step 8: Completion

In this final step, after completing all its tasks, the agentic AI produces the expected result. The agentic AI then returns to its initial state (awaiting new instructions).

Agentic AI: Different configurations

In this section, we will discuss the different types of configurations that can be achieved with agentic AI.

There are two types of configurations, referred to as “Single Agent” and “Multi-Agent.”

The simplest configuration, “Single Agent,” consists of a single agentic AI.

The “Multi-Agent” architecture consists of three sub-configurations: Vertical architecture, Horizontal architecture, and Hybrid architecture.

• Vertical architecture

This multiagent architecture consists of a main agent and is connected to two or more (subordinate) agentic AIs.

• Horizontal architecture

This multi-agent architecture consists of three AI agents. Each AI agent operates transversally with the others.

• Hybrid architecture

This multi-agent architecture consists of a main AI agent with a group of AI agents (horizontal architecture) and a single AI agent (single agent).

Agentic AI: Different layers

In the previous sections, we discussed the definition of Agentic AI. Then, we discussed what characterizes Agentic AI. In this new section, we will dig a little deeper by discussing the architecture of Agentic AI.

The number of layers present in the architecture of Agentic AI is a controversial issue. Not because of what it consists of, but rather how it is presented.

From an architectural standpoint, agentic AI consists of 5, 7, or 8 layers. The number of layers depends on the observer’s point of view. By “the observer’s point of view,” we mean who is viewing the architecture.

In concrete terms, if we were to put ourselves in the place of Agentic AI, we would have an architecture composed of five layers.

• Architecture as seen by an agentic AI

1. Input/Perception

2. Memory

3. Reasoning/Planning

4. Action/Execution

5. Supervision/Evaluation

If we put ourselves in the place of a human being, we would have a 7- or 8-layer architecture.

• Architecture as seen by a human being

1. Data/Environment

2. Foundational model

3. Memory/Context

4. Reasoning/Planning

5. Tools/Actions

6. Orchestration

7. Supervision/Human interface

After addressing the issue from a perspective, there are other reasons why the number of layers differs depending on the presentation.

The first reason is that there is no official standard defining the different layers of Agentic AI architecture. It’s like a hamburger: there are countless shapes and varieties, but no original recipe.

The second reason is that it also depends on the context in which an Agentic AI architecture diagram is presented (novice or experienced the audience) and on the desired level of granularity. Most diagrams consist of seven layers.

In our case, we will present the Agentic AI architecture in seven layers. We will begin the presentation of the architecture with layer 1, which is the lowest layer in the Agentic AI architecture.

It should be noted that in practice, the different layers of the Agentic AI architecture are not stacked on top of each other. Some layers are parallel to other layers. For the sake of clarity in the presentation, we have chosen to display the layers on top of each other.

• Layer 1: Infrastructure Layer

The first layer corresponds to the infrastructure layer. This is the layer on which the Agentic AI is stored. But it is also the layer used for calculating algorithms and transferring data with network access. It is also the orchestration layer of the Agentic AI.

• Layer 2: Data and Perception Layer

The second layer enables information to be collected. Using APIs, the Agentic AI system connects to various tools, enabling it to use these tools to improve its training models. Again using APIs, the Agentic AI system interacts with other applications (booking, task execution, etc.).

• Layer 3: Representation and Context

The third layer is used to establish context. In this layer, the agentic AI system translates human language into machine language. The agentic AI then takes various additional elements into account to better understand the initial request and optimize the expected result.

• Layer 4: Memory and Retrieval

The fourth layer is very important. First, with the “memory” sublayer, it allows the agentic AI to “remember” previously acquired information. With the information in memory, the agentic AI will be able to remember what it needs to do. The memory layer serves as a history and provides context.

As for the “retrieval” sublayer, this is RAG—Retrieval-Augmented Generation. The purpose of RAG is to provide more recent information on LLMs—Large Language Models. RAG acts as a module that updates an LLM without impacting the structure of the LLM.

Very broadly speaking, you can compare RAG to a database update. You have a database with x data. With the addition of a module (in our example, this will be a package), you can have new data without impacting the initial data in the database.

• Layer 5: Cognition, Planning, and Reasoning

The fifth layer can be broken down into three sub-layers and enables results to be generated in the form of a logical and coordinated sequence of actions.

This layer enables a coherent result to be generated based on the instructions requested.

This layer also includes the “explainability” component.

• Layer 6: Tool Orchestration and Execution

The sixth layer is used by Agentic AI to communicate with Application Programming Interfaces (APIs). By using APIs, Agentic AI communicates with other applications or systems.

• Layer 7: Feedback Loop and Status Feedback

The 7th layer is divided into four sub-parts: Observation; Evaluation; Memorization; and Adjustment.

The seventh and final layer of the Agentic AI architecture enables the agent to develop autonomous and continuous learning. This form of learning does not allow Agentic AI to learn by modifying its source code. However, it does enable it to improve its future results. It is a self-correcting mechanism or behavioral learning.

Professionals among our readers will have noticed that one layer is missing compared to other diagrams available on the Internet. This omission is intentional, as the Muse™ editorial team believes that this step must be performed on all of the different layers.

All elements relating to AI explainability (audit, human control, reasoning, traceability, etc.) must be taken into account from the design stage of any project involving AI systems.

AI explainability must be possible on every layer of an AI Agent architecture. This is why you will not find a Governance and Observability layer on our presentation.

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