What Are Generative AI Agents? Comprehensive Guide

A complete guide to generative AI agents: Discover their architecture, functionality, and why they represent the latest trend in artificial intelligence.

Fabio Silva

11/22/202415 min read

Generative AI Agents

Generative AI agents are transforming the way we interact with technology, bridging the gap between static models and dynamic, goal-oriented systems. At their core, these agents are applications designed to achieve specific objectives by observing the world around them and taking action using the tools at their disposal. Unlike traditional models, which primarily provide responses based on pre-trained data, generative AI agents actively engage with their environment, making decisions and adapting in real time.

The term generative AI agent reflects a shift in focus from models to applications. While large language models (LLMs) like ChatGPT or Gemini provide the foundation, it is the agent's ability to observe, reason, and act that defines its functionality. Generative AI agents are no longer just repositories of information but active participants in solving problems, executing tasks, and fulfilling user needs.

What is a generative AI agent?

A generative AI agent is defined as an application that tries to achieve a goal by observing the world and acting upon it using the tools it has at its disposal.

The capacity to interact with humans and perform autonomous tasks—makes generative AI agents versatile tools for a wide range of applications. They can respond dynamically to changing contexts, learn from their experiences, and integrate seamlessly with external tools and systems.

As we dive deeper into the mechanics of generative AI agents, we’ll explore the components that enable their operation, the tools they use to interface with the world, and the architectures that underpin their versatility. These agents represent the next evolution in AI, moving beyond static systems to create intelligent, goal-driven applications that adapt and act in complex environments.

1. GenAI Agent Interactions Types

Generative AI agents can be grouped into two main types: conversational agents and workflow agents. These categories highlight their adaptability, whether interacting directly with users or operating autonomously to manage backend processes.

Conversational Agents

Conversational agents are perhaps the most well-known type of generative AI agent, with applications like ChatGPT, Gemini, Perplexity, and others gaining widespread recognition. These agents are designed to interact with users through natural language interfaces, answering questions, assisting with tasks, or providing detailed explanations in real time.

The strength of conversational agents lies in their ability to interpret user queries and respond contextually, making interactions feel fluid and human-like. Because of their versatility, conversational agents are commonly used in customer support, education, virtual assistance, and creative applications like brainstorming or content generation. Their reliance on large language models enables them to excel in understanding and generating meaningful text, making them an integral part of many user-facing solutions.

Workflow Agents

Workflow agents are designed to operate autonomously, often without any direct human interaction. Instead of responding to user queries like conversational agents, workflow agents are triggered by specific events or conditions within a system. These agents excel at automating repetitive or time-sensitive tasks, making them indispensable for backend operations.

Workflow agents are often compared to systems in Robotic Process Automation (RPA). Like RPA solutions, they are programmed to handle predefined workflows, such as processing transactions, updating records, or monitoring system alerts. However, workflow agents go further by leveraging AI to make decisions dynamically based on real-time data.

These agents integrate effectively with external systems through APIs and other tools, enabling them to fetch, process, and act on information as required. For example, a workflow agent in a finance system might monitor payment statuses, flag overdue invoices, and trigger automated follow-up reminders.

By handling these operational tasks autonomously, workflow agents free up human resources to focus on more strategic activities. Their ability to act without human input makes them a cornerstone of modern automated systems, enhancing efficiency and reducing the likelihood of errors in routine processes.

2. Foundational Components of AI Agents

Let's break down these generative AI agents into foundational components.

Generative AI agents are built on three foundational components that work together to help the agent understand requests, make decisions, and take actions. These components are essential for the agent’s ability to process input and respond effectively, whether interacting with a user or handling automated workflows.

At a basic level, an agent operates by receiving a query—either from a human or an API—and providing a response. To accomplish this, the agent uses:

The Model: This is the agent’s core processing unit, responsible for interpreting the query, reasoning about goals, and generating appropriate responses.
The Reasoning Loop: This allows the agent to think step-by-step, observe its environment, and adjust its actions based on both short-term and long-term memory.
The Tools: These enable the agent to access external data or services, such as fetching information through APIs or performing specific actions.

Each component plays a distinct role, yet they are designed to work in harmony. Together, they enable AI agents to operate intelligently and adapt to a variety of tasks and environments.

So this is kind of what the agent runtime is all about and then we will be just diving deep into all these three components separately.

I put this picture here just to make it a little bit more simple, that we have three components rebranded as reasoning loop, tools and model, okay?

Let's dive into each one of them.

2.1 The Role of Models in AI Agents

The models are just the things that we know already, like this can be Gemini Pro/Flash, OpenAI GPTs, your Tune models and any external model available. These are just something that you are already using today. So there is not much of an elaboration needed for this one. They just play the execution side of the agent itself. So they just orchestrate information coming from the reasoning loop, they execute on it and then they take action alongside as well.

2.2 Tools used by AI Agents

If you want to look at the tools themselves, we have three types of tools, we have Extensions, we have Functions and we have Data Stores. And then what those three tools do, they allow agents to interact with the external data and services.

Just to simply, when you ask a question to an LLM model asking what is two plus two, it shouldn't just dig through its own knowledge base and then come up with the answer like it is four, but it should know how to use a calculator, and use that as a tool and then send the command of push the button two and push the button plus and then two again and then equal and get the result of four. Understand what it does and then respond by saying it is equal to four.

This is a very simplified version of what the tools essentially do, this is one example of tools where it is talking to an external party of APIs, but this can also just extract data. So these are the three types of tools which we'll be further digging down.

2.3 The Reasoning Loop

The reasoning loop is where the most interesting part of generative AI agents happens. This component differentiates the agent framework from anything that came before it. So, what exactly does the reasoning loop do?

The reasoning loop is responsible for setting goals, interpreting incoming requests, and connecting complex thought chains. It enables the agent to think iteratively, break down problems into manageable chunks, and devise a plan of action before arriving at a conclusion. This iterative process mirrors how humans process information. When asked a question, we don’t simply respond immediately; we assess, plan, and execute our response in a structured manner. The reasoning loop applies this same concept to AI agents.

To understand the process, let’s break it down:

Goal Setting and Instructions: The reasoning loop starts with the agent setting a goal based on the user’s query. It uses predefined instructions or short- and long-term memory to understand the context of the task.
Planning and Execution: Once the goal is clear, the agent devises a plan. It uses its reasoning capabilities to determine what needs to be done step by step. For example, if the query involves booking a flight, the agent plans to search for flights, retrieve options, and present them to the user.
Tool Utilization: The reasoning loop coordinates with tools to perform specific actions, such as fetching data or interacting with APIs. For example, it might calculate a result using an external calculator or fetch flight details from a dedicated flight booking API.

The reasoning loop also includes advanced algorithms that enable iterative and nuanced problem-solving. Some popular methods include:

Chain of Thought (CoT): Breaking down problems into logical steps.
ReAct: Combining reasoning and acting, which enhances the agent's ability to perform tasks interactively.
Tree of Thoughts: Exploring multiple solution paths simultaneously before deciding on the best one.

Directional Streamers Prompting: Leveraging prompts that guide the agent’s thought process in a focused direction.

Among these, one particularly notable framework is the ReAct agent. It’s known for its ability to dynamically reason and act, embedding these processes into real-world applications. Originally introduced in a 2021 research paper, this framework has since become a cornerstone for many sophisticated AI agents.

In essence, the reasoning loop is what allows generative AI agents to bridge the gap between static model outputs and dynamic, context-sensitive responses. It is not merely about executing commands but understanding and reasoning through complex problems to deliver accurate and contextually relevant results.

3. Agents Operations - A Practical Example

To grasp how an AI agent operates in real-world scenarios, let’s walk through a practical example: booking a flight. This example illustrates how the core components—reasoning loop, tools, and model—work together to process a user query and deliver actionable results.

The process begins when a user provides an input: "I want to book a flight from Austing to Zurich". This query is routed to the agent, which identifies the intent behind the request and initiates its reasoning process.

The reasoning loop processes the user’s query. It begins by setting a goal, which is to book a flight. The loop identifies sub-tasks required to achieve this goal like:

Search for available flights.
Filter and sort options based on user preferences (e.g., time, price, or airline).
Present the best options to the user.

This structured breakdown allows the agent to plan its next steps logically and efficiently.

To execute these sub-tasks, the reasoning loop determines which tools are required. For example:

Flight Search Tool: Queries an external flights database (e.g., via a flight booking API).

The reasoning loop triggers the necessary tools, observes the returned data and evaluates its relevance. For instance, if no flights are available, the agent might adjust its query or notify the user of alternative options.

With the flight options retrieved, the agent crafts a response to the user. For example:
"Here are the best flight options from Austin to Zurich on December 15, 2024:"

Option 1: Airline A, $500, 1-stop, 10:00 AM departure.
Option 2: Airline B, $450, 2-stops, 8:30 AM departure.

The agent communicates the results clearly and waits for further instructions, such as booking one of the options or refining the search criteria.

This example is the operational flow of a very simple single agent, demonstrating how it transforms a simple query into actionable results. By combining its reasoning capabilities, tools, and model execution, the agent not only responds to user inputs but also adapts dynamically to achieve its goal.

4. Extensions, Functions and Data Stores

To enable AI agents to interact with external systems and perform meaningful actions, they rely on tools. In the context of AI agents, tools can take several forms, such as extensions, functions, and data stores. These components bridge the gap between the agent's internal capabilities and the external environment, allowing it to fetch, process, and act on data effectively. Let’s explore each of these in detail.

4.1 Extensions: Direct Connections to External APIs

Extensions are tools that allow agents to connect directly to external APIs and systems. They act as conduits for the agent to send and receive information without requiring additional layers of complexity.

For example, imagine an agent needs to retrieve flight details using the Google Flights API. An extension would enable the agent to send a "get flights" request directly to the API with the required parameters, such as departure location, destination, and date. The API processes the request and returns a list of flights, which the agent uses to craft its response.

Direct API Integration: Extensions are embedded directly into the agent’s architecture, eliminating the need for intermediaries.
Open Standards: Often use formats like OpenAPI (formerly Swagger) to define how the agent interacts with external services.
Use Cases: Extensions are ideal for straightforward interactions where security concerns, like sensitive credentials, are not a major issue.

4.2 Functions: Middleware for Secure and Controlled Access

In some scenarios, direct integration via extensions may not be feasible or secure. For example, when working with sensitive APIs that require secret keys or involve restricted data, exposing these details directly to the agent could pose security risks. This is where functions come into play.

Functions act as middleware, serving as a secure intermediary between the agent and external services. Instead of allowing the agent direct access to an API, the function handles the interaction on behalf of the agent.

Advantages of Functions:

Enhanced Security: Sensitive data (like API keys) is managed by the middleware and never exposed to the agent.
Access Control: Functions enforce additional layers of permission and validation.
Abstraction: Simplifies the agent’s design by offloading complex or sensitive operations to middleware.

AI Agents, comparison between Extensions and Functions

4.3 Data Stores: Structured Information Retrieval

Data stores enable agents to retrieve and process structured data efficiently. Unlike extensions or functions, which focus on interactions with APIs, data stores are repositories of information that the agent can query directly.

The most common example is a vector database, which stores embeddings (numerical representations of data) for rapid similarity searches. This allows agents to handle large volumes of information and retrieve the most relevant data based on a user query.

For instance, an agent might query a vector database to find the answer to: “What colors does the Pixel 7 come in?” The database performs a nearest-neighbor search on the embeddings of stored data and returns a relevant result.

AI Agents. Data Stores: Structured Information Retrieval

Applications of Data Stores:

Private Data Retrieval: Access to proprietary or internal documents.
Public Knowledge Bases: Querying structured data from websites or APIs.
Dynamic Learning: Enabling the agent to learn from new data by updating the embeddings.

4.4 How These Tools Work Together

The true power of extensions, functions, and data stores lies in how they integrate into an agent’s reasoning process. Together, they form a toolkit that allows the agent to:

Interact with APIs: Extensions for quick access to external data.
Handle Sensitive Operations: Functions to protect secure processes.
Retrieve Contextual Information: Data stores for embedding-based searches.

For example, consider an agent helping a user plan a trip:

It uses an extension to query a public flight API for available tickets.
It employs a function to access a secure payment gateway, ensuring the user’s credentials remain protected.
It queries a data store for local hotel options based on previously stored reviews or recommendations.

Each of these tools complements the other, creating a seamless and robust interaction model for the agent.

5. Architectures for AI Agents

AI agents can operate within a variety of architectures, each tailored to the complexity of the tasks they are designed to perform. As we saw in this article, at the simplest level, single-agent architectures handle straightforward queries and tasks independently. For more sophisticated or large-scale applications, however, multi-agent architectures take center stage. These systems employ multiple specialized agents that collaborate or operate under a coordinated structure to solve problems efficiently.

5.1 Simple Agent Architecture

5.2 Multi-Agent Architectures

Multi-agent architectures are built to divide labor across specialized agents, each focusing on specific subtasks. This division of work enables better performance, scalability, and flexibility, particularly for complex operations that a single agent would struggle to manage. Multi-agent systems come in two primary forms: collaborative and supervisory.

Collaborative Multi-Agent Systems

In a collaborative multi-agent system, each agent independently works on a specific part of a larger task, and their outputs are combined to create the final result. This system thrives in scenarios where tasks can be broken into smaller, independent parts that don’t require centralized control.

The advantage of this approach is efficiency. By allowing multiple agents to work simultaneously, tasks are completed faster. Additionally, specialization ensures each agent is optimized for its specific role, leading to higher-quality outputs.

For example, imagine planning a vacation. One agent searches for flights, another recommends hotels, and a third suggests local activities. Each agent works independently, and their results are merged into a comprehensive plan for the user. This collaborative approach makes the system highly efficient and modular, as individual agents can be improved or replaced without disrupting the entire operation.

Supervisory (Top-Down) Multi-Agent Systems

Supervisory systems use a hierarchical structure, with a central orchestrator agent managing several specialized sub-agents. The orchestrator receives a task, divides it into smaller pieces, assigns these to the sub-agents, and then combines their outputs into a unified result.

The primary advantage of this approach is coordination. The orchestrator ensures that all agents work toward a common goal, even if their subtasks are interdependent or complex. This structure also allows for dynamic adjustments, as the orchestrator can reassign tasks based on real-time feedback from the sub-agents.

Supervisory (Top-Down) Multi-Agent Systems

A practical example of this system is a customer service chatbot for an online store. When a user asks, “Where is my order?”, the orchestrator assigns subtasks to different sub-agents: one checks shipping status, another retrieves order details, and a third reviews any customer service notes. Once the sub-agents complete their tasks, the orchestrator compiles the information into a single response, such as, “Your order shipped on November 17 and will arrive on November 20.”

Multi-agent capabilities

Multi-agent architectures extend the capabilities of AI agents, enabling them to tackle tasks that go beyond the limits of single-agent systems. Collaborative systems excel in dividing independent tasks among agents, while supervisory systems bring coordination and oversight to complex or interdependent processes. Together, these architectures offer flexible, scalable, and efficient solutions for a wide range of real-world applications, from vacation planning to customer service. As AI technologies advance, these architectures will play a crucial role in enabling agents to solve increasingly complex challenges.

6. Collaborative vs. Supervisory Multi-Agent Systems

When tasks become complex or multifaceted, relying on a single AI agent may not be enough. Multi-agent systems provide a way to distribute work across multiple specialized agents, which can either collaborate or operate under a coordinated supervisory structure. Understanding the distinction between collaborative and supervisory multi-agent systems highlights their unique strengths and the scenarios where each excels.

Collaborative Multi-Agent Systems

Collaborative systems are designed for scenarios where agents can work independently on different parts of a task and combine their outputs to produce the final result. These systems rely on the principle of decentralization, where each agent has a specific role and is largely autonomous in executing it.

The key advantage of collaboration is speed and scalability. Since agents operate simultaneously, complex tasks can be broken into smaller subtasks that are processed in parallel. This modularity also makes it easy to add or update agents without disrupting the overall system.

For example, consider a multi-agent system tasked with creating a business proposal. One agent collects market data, another analyzes financial projections, and a third drafts the presentation. Each agent works independently, and their outputs are merged into a single, comprehensive document. This decentralized approach ensures efficiency, particularly when subtasks are distinct and do not require constant coordination.

However, collaborative systems may face challenges when tasks are interdependent, as they lack a central mechanism to oversee and adjust how agents interact. This makes them ideal for tasks where each agent's work is clearly defined and does not overlap significantly with others.

Supervisory Multi-Agent Systems

In contrast, supervisory systems are built around a central agent, often called the orchestrator, which manages the activities of subordinate agents. The orchestrator divides the primary task into subtasks, assigns them to specific agents, and then consolidates the outputs into a coherent result.

The main advantage of this structure is coordination. The orchestrator ensures that agents work in harmony, especially when their tasks are interconnected. This hierarchical setup also provides a mechanism for error handling and dynamic adjustments, as the orchestrator monitors the progress of each agent and reallocates tasks as needed.

For example, a logistics system might use a supervisory approach to optimize supply chain operations. The orchestrator agent oversees the process, assigning subtasks like inventory checks, route optimization, and delivery scheduling to specialized sub-agents. It consolidates their findings into a single plan, ensuring that the entire supply chain functions smoothly.

Supervisory systems excel in handling tasks with complex dependencies but may introduce inefficiencies when too much decision-making is centralized. Nonetheless, their ability to adapt dynamically to evolving situations makes them invaluable for tasks requiring tight coordination.

7. Conclusion

Generative AI agents represent a revolutionary leap in how technology interacts with and impacts the world. By combining reasoning, adaptability, and tool integration, these agents can handle tasks ranging from simple conversations to complex workflows. Through architectures like single-agent setups, collaborative multi-agent systems, and supervisory frameworks, they offer scalable solutions tailored to various needs.

As we explored, their foundational components—reasoning loops, tools, and models—allow them to think, plan, and act dynamically. Whether using extensions, functions, or data stores, or leveraging the power of multi-agent cooperation, these agents are paving the way for more intelligent, flexible, and robust AI-driven systems.

The journey of AI agents has just begun, and their evolving architectures promise to tackle increasingly sophisticated challenges. From improving business efficiency to enhancing everyday life, these systems are set to become indispensable tools in our rapidly advancing technological landscape.

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