Anatomy of an AI Agent

Anatomy of an AI Agent

An AI agent is a system capable of perceiving its environment, reasoning, and executing tasks autonomously through language models (LLMs), tools, and an orchestration layer.

Already at the heart of numerous use cases, from customer support to real-time data analysis, these intelligent agents represent a major evolution in artificial intelligence. According to Gartner, more than 30% of AI applications will integrate autonomous agent mechanisms by 2026 , compared to less than 5% in 2023.

In this article, we detail their architecture, their decision-making logic , and their concrete applications to understand how to create agents with high added value for the company.

But what exactly is an AI agent?

An intelligent agent is a software system capable of achieving a goal by analyzing data, making decisions, and acting with limited or no human intervention.

The general idea is quickly grasped: intelligent entities that perceive their environment, think and act to accomplish tasks.

The key components for creating reliable autonomous agents capable of handling complex tasks are as follows:

• Cognitive architectures : It explores paradigms such as ReAct (Reasoning and Acting), which combines explicit reasoning and contextual actions, or Chain-of-Thought models which allow AI to structure more complex and iterative reasoning.
• Extensions and functions : Agents can be enhanced with modular extensions, allowing them to interact with specific tools, such as APIs or databases. These extensions act as "superpowers" tailored to a company's needs.
• Knowledge base (RAG and others) : Agents often rely on techniques like RAG (Retrieval-Augmented Generation), which combines generative AI and searching structured databases to produce reliable and contextual answers.

How does an AI agent work?

An agent relies on a continuous cycle: natural language understanding (NLP) , planning, action via tools, and learning from results.

Its operation comprises three main components:

• The Language Model (LM) : At the heart of the agent, the language model is the central decision-making engine. It can be one or more models of varying sizes, capable of following instruction-based reasoning and logic patterns, such as ReAct, Chain-of-Thought, or Tree-of-Thoughts. This model allows the agent to interpret requests, plan its actions, and generate responses.
• Tools : Tools are the keys that allow the agent to interact with the outside world. These tools can be extensions, functions, or data stores, each with a specific function. They allow the agent to access real-time information, manipulate data, and perform concrete actions.
• The orchestration layer : This layer defines a cyclic 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 scheduling, and it relies on prompt engineering techniques to improve the agent's efficiency.

The essential role of tools: extensions, functions and data stores

They allow the agent to interact with the company's information system, business databases or social networks in order to automate high value-added processes.

In this universe 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 seamlessly execute API calls regardless of their implementation. They are defined by examples that guide the agent in their use and in choosing the appropriate extension for the task at hand. For example, the Google Flights extension allows the agent to book flights.

Functions , on the other hand, differ from extensions in that they execute on the client side rather than the agent side. This behavior transfers the logic and execution of API calls to the client application , thus providing greater control over the data flow. They are particularly useful when APIs are not directly accessible by the agent.

Data stores are databases, structured or unstructured, that allow the agent to access dynamic and up-to-date information , thus exceeding the limitations of its initial training data. Generally implemented using vector databases, they facilitate the use of techniques such as Retrieval Augmented Generation (RAG).

Orchestration: planning, reasoning, and action

The orchestration layer is the operational brain of the intelligent agent: it organizes reasoning, action planning, and memory management in a continuous cycle. This layer relies on reasoning techniques such as:

• ReAct : a prompt engineering framework that allows the model to reason and act in response to a user request, using actions and observations to refine its understanding.
• Chain-of-Thought (CoT) : a framework that allows the model to reason through intermediate steps, which improves its ability to solve complex problems.
• Tree-of-Thoughts (ToT) : a generalization of CoT, which allows the model to explore multiple chains of reasoning, which is ideal for exploratory tasks or strategic problems.

The orchestration layer uses these techniques to organize the agent's information, reasoning, and action cycle.

Concrete example of an AI Agent lifecycle

Here is an example of a travel planning agent:

1. Request reception : the agent receives a request from the user, for example: "I want to book a flight from Paris to New York".
2. Query analysis : The model (LM) analyzes the query and identifies the user's intent (book a flight), parameters (departure city and destination), and other relevant information.
3. Tool selection : the agent, guided by the orchestration layer, selects the appropriate tool (the Google Flights extension) to access flight information.
4. Using the tool : The agent uses the extension to perform a flight search using the parameters specified by the user.
5. Response processing : The agent receives a response from the Google Flights API with a list of available flights.
6. Reasoning and selection : the model analyzes the results and uses logic (ReAct or CoT) to choose the flight that best matches the user's preferences.
7. Presentation of the result : the agent presents a clear and concise answer to the user, for example: "Here are the available flights from Paris to New York...".

Some other professional examples:

These systems are particularly relevant when an organization needs to perform complex tasks on a large scale with a high level of personalization.

• Trading : agents who analyze financial markets and execute orders.
• Customer service: agents capable of handling requests independently, improving the customer experience and reducing response times while handing over complex cases to a human.
• Information retrieval : agents exploring large volumes of data to extract relevant insights, relying on the RAG.
• Predictive maintenance : agents anticipating potential breakdowns through data analysis.

The role of learning

Learning allows AI agents to improve their autonomy and adapt their behavior to specific business contexts. Several strategies exist for this purpose:

• In-context learning : allows the agent to learn new tools and tasks using prompts, examples, and instructions provided at the time of inference.
• 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, in order to improve its ability to perform certain tasks or to choose the right tools.

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Autonomous agents are already transforming operational models by enabling the automation of processes, accelerating decision-making, and creating new services based on generative AI.

At Smile, our experts   Data and AI design and integrate intelligent agent architectures tailored to your business challenges, from ideation to production.