Discover the anatomy of AI agents, from their architecture to concrete examples, and explore their potential to transform your organization.
AI agents are revolutionizing the way we interact with artificial intelligence. Capable of perceiving their environment, reasoning, and acting autonomously, these systems rely on advanced cognitive architectures and specialized tools to perform complex tasks. But how do they actually work? What are the key technologies behind these intelligent agents? In this article, we break down the anatomy of an AI agent: its core components, reasoning methods, and real-world use cases.
But what exactly is an AI agent?
The general idea is easy to grasp: intelligent entities that perceive their environment, think, and act to accomplish tasks.
While the concept is appealing, it can feel abstract when it comes to real-world implementation. And once you begin building them, complex questions emerge: which architecture should you choose, what patterns should you apply, which frameworks are most suitable?
To build robust and high-performing AI agents, several key components are essential:
- Cognitive architectures: These include paradigms like ReAct (Reasoning and Acting), which combines explicit reasoning with contextual actions, or Chain-of-Thought models that help AI structure more complex and iterative reasoning processes.
- Extensions and functions: Agents can be enhanced through modular extensions that allow them to interact with specific tools such as APIs or databases. These extensions act like “superpowers” tailored to an organization’s needs.
- Knowledge base (RAG and others): Agents often rely on techniques like RAG (Retrieval-Augmented Generation), which combines generative AI with search capabilities across structured databases to generate accurate and contextual responses.
How does an AI agent work?
The functioning of an AI agent relies on a structured architecture composed of three main components:
- The model (Language Model - LM): At the core of the agent, the language model is the central decision engine. This can be one or several models of varying sizes, capable of following reasoning and logic patterns based on instructions, such as ReAct, Chain-of-Thought, or Tree-of-Thoughts. The model enables the agent to interpret requests, plan its actions, and generate responses.
- The tools: Tools are the keys that allow the agent to interact with the external world. These tools can include extensions, functions, or data stores, each serving a specific purpose. They allow the agent to access real-time information, manipulate data, and carry out concrete actions.
- The orchestration layer: This layer defines a cyclical process that guides how the agent processes information, performs internal reasoning, and uses that reasoning to determine its next actions. It manages the agent’s memory, state, and planning, and leverages prompt engineering techniques to improve the agent’s efficiency.
The essential role of tools: extensions, functions, and data stores
In the world of intelligent agents, several tools play a key role in expanding their capabilities and enabling them to interact effectively with their environment.
Extensions act as bridges between an agent and an API, allowing the agent to execute API calls seamlessly, regardless of how they are implemented. They are defined by examples that guide the agent in using them and choosing the appropriate extension based on the task to be accomplished. For instance, the Google Flights extension enables the agent to book flights.
Functions, on the other hand, differ from extensions in that they are executed on the client side rather than by the agent itself. This setup shifts the logic and execution of API calls to the client application, offering better control over the data flow. They are especially useful when APIs are not directly accessible by the agent.
Data stores are databases—structured or unstructured—that give the agent access to dynamic, up-to-date information, going beyond the limits of its initial training data. Often implemented using vector databases, they facilitate the use of techniques such as Retrieval-Augmented Generation (RAG).
Orchestration: planning, reasoning, and action
At the heart of an AI Agent’s operation lies the orchestration layer—a cognitive architecture that structures reasoning, planning, decision-making, and guides the agent’s actions. This layer relies on reasoning techniques such as:
- ReAct: A prompt engineering framework that enables the model to reason and act in response to a user query, using actions and observations to refine its understanding.
- Chain-of-Thought (CoT): A framework that allows the model to reason through intermediate steps, enhancing its ability to solve complex problems.
- Tree-of-Thoughts (ToT): A generalization of CoT, which enables the model to explore multiple reasoning paths—ideal for exploratory tasks or strategic problem-solving.
The orchestration layer uses these techniques to structure the agent’s information-processing, reasoning, and action cycle.
Concrete example of an AI Agent lifecycle
Here’s an example of a travel planning agent:
- Request reception: The agent receives a user request, for example: “I want to book a flight from Paris to New York.”
- Request analysis: The language model (LM) analyzes the request and identifies the user's intent (booking a flight), parameters (departure and destination cities), and other relevant information.
- Tool selection: Guided by the orchestration layer, the agent selects the appropriate tool (the Google Flights extension) to access flight information.
- Tool usage: The agent uses the extension to search for flights based on the parameters provided by the user.
- Response processing: The agent receives a response from the Google Flights API with a list of available flights.
- Reasoning and selection: The model analyzes the results and applies logic (ReAct or CoT) to choose the flight that best matches the user’s preferences.
- Result presentation: The agent provides a clear and concise response to the user, such as: “Here are the available flights from Paris to New York…”
A few other business use cases:
- Trading: Agents that analyze financial markets and execute orders.
- Customer service: Agents capable of answering questions, handling requests, and resolving issues without human intervention.
- Information retrieval: Agents that explore large volumes of data to extract relevant insights, leveraging RAG techniques.
- Predictive maintenance: Agents that anticipate potential failures through data analysis.
The role of learning
Learning is essential for AI agents. Several strategies make this possible:
- In-context learning: Enables the agent to learn new tools and tasks using prompts, examples, and instructions provided at inference time.
- Retrieval-based in-context learning: The agent retrieves relevant examples from its external memory to better adapt to the user’s request.
- Fine-tuning based learning: Allows the model to be trained on specific examples, improving its ability to perform certain tasks or select the right tools.
Curious to experiment with an AI Agent? Let’s work together!
AI agents represent a tangible application of generative AI. Their ability to reason, interact with the real world through tools, and learn dynamically gives them strong transformational potential.
At Smile, we firmly believe in the central role AI agents will play in the future of digital transformation. Backed by our expertise in Data and AI, we are actively exploring the many facets of this technology to support your transition. From designing robust cognitive architectures to integrating AI agents into your business processes, we provide a tailored approach grounded in solid experience and a deep understanding of your industry’s specific challenges.
To dive deeper, we recommend our latest white paper: “Building an Open Source AI Application.”