What is an AI Agent?
Bernard Aceituno
@bernaceituno
Have you ever wished your AI assistant could do more than just set reminders or tell you the weather? Like, what if it could handle your inbox, schedule meetings, and maybe even help you tackle that never-ending to-do list? Welcome to the world of AI agents, where AI acts autonomously to solve problems and get work done. These proactive AI agents don’t require constant input; they seize control, figure out the next steps, and get things done independently.
While chatbots and virtual assistants are great for quick tasks, AI agents operate on a whole different level. They’re the ideal collaborators, able to simplify complex jobs, gather critical details, and get things done without you having to step in constantly. Imagine an AI that reads your emails, categorizes them, responds when appropriate, and updates your CRM system—all while you focus on the bigger picture.
In this article, we’ll dive deep into AI agents, how they work, and why they’re poised to revolutionize everything from business automation to everyday tasks. Whether you're just curious or ready to embrace the next evolution in AI, you’re sure to learn a lot about the rapidly developing world of AI agents. Let’s get started!
Table of content
2. AI agents vs. AI chatbots and AI assistants
1. What is an AI Agent?
An AI agent is a software program capable of acting autonomously to achieve specific goals. Unlike traditional programs that follow fixed instructions, AI agents perceive their environment, interpret data, and adapt their behavior to complete tasks without continuous user input. This autonomy allows them to operate independently, solving problems and making decisions as they interact with their environments
AI agents vs. AI chatbots and AI assistants
In the rapidly changing world of AI, keeping up with the terminology can be difficult. The terms AI agents, AI chatbots, and AI assistants are often used interchangeably, although they each have slightly different definitions. Unlike familiar AI chatbots like ChatGPT and AI assistants, AI agents are more autonomous and goal-oriented. AI chatbots typically respond to user prompts, lacking the ability to act independently. Similarly, AI personal assistants like Siri or Alexa can perform tasks based on user commands but still rely heavily on direct input and instructions to function. In contrast, AI agents are given a goal and then proactively manage tasks and make decisions independently, operating without constant user supervision. For instance, while an AI assistant might set a reminder or provide information when asked, an AI agent could monitor an email inbox and automatically schedule meetings on your behalf.
While all three—AI agents, AI chatbots, and AI assistants—leverage large language models (LLMs) to perform tasks, there are important distinctions in how they operate. AI chatbots, such as ChatGPT, are primarily designed to respond to specific user prompts, generating text-based answers or completing tasks based on direct input. They lack the ability to act independently, meaning they require constant interaction to function.
For example, in the image below, you can see a user interacting with ChatGPT by requesting it to generate a Python script. A full AI agent would also be able to execute the code and perform follow-up actions. The AI space is constantly evolving, and ChatGPT is already making strides toward agent-like behavior. ChatGPT can now execute code via the code interpreter function, and we can expect further developments to increase agentic behavior within ChatGPT.
AI assistants, like Siri or Alexa, are slightly more advanced in that they can perform a range of tasks (e.g., setting reminders, sending messages) based on voice or text commands. However, these AI personal assistants also are reactive, relying heavily on user input to initiate actions. While they enhance daily convenience, they are still bound to user-driven commands and don’t possess the capability to proactively work toward long-term goals.
AI agents, by contrast, are more autonomous and goal-oriented. Technically speaking, an AI agent can break down a complex task into multiple subtasks and then execute each one in sequence. Once given an objective, AI agents manage tasks independently, without needing continuous user interaction. For example, an AI agent in a business setting could be tasked with reviewing incoming emails, extracting relevant data, and automatically updating a CRM system. The agent would proactively analyze each email, categorize it, extract key information like client details or requests, and input that data into the appropriate fields, all without requiring manual oversight. This level of autonomy allows the agent to handle repetitive administrative tasks efficiently, freeing up human workers for higher-level strategic work.
Given the current state of the art (i.e., still in the early days), it can be generalized that all three—AI agents, AI chatbots, and AI assistants—are intelligent agents with instructions, performing tasks in different ways. While their core technology may be similar, the key difference lies in how much autonomy and decision-making power they possess. AI agents stand out for their ability to act independently toward long-term goals, making them more versatile and capable in dynamic, evolving environments.
2. How do AI Agents Work?
AI agents operate through a structured process that allows them to autonomously set and complete goals. At a high level, this process involves determining an objective, gathering relevant information, outlining tasks, and performing actions to achieve the desired outcome. Unlike traditional programs that follow static instructions, AI agents can dynamically adapt their approach based on new data and changing circumstances.
First, an AI agent determines its goal, which is typically set by a user or an external trigger. This goal could be as simple as categorizing incoming emails or as complex as analyzing a large set of financial data for insights. Once the objective is established, the agent acquires the necessary background information, such as pulling data from a company’s database or performing real-time internet searches. The agent uses this information to make informed decisions on how best to approach its task.
Next, the agent outlines the necessary tasks required to reach its goal. It breaks down the objective into smaller, manageable steps, creating a plan of action. For instance, an AI agent tasked with analyzing financial reports might identify tasks like retrieving specific documents, extracting relevant figures, and running comparisons across multiple data sets. Finally, the agent performs these tasks autonomously, following the plan it formulated. As the agent progresses, it continuously monitors its progress and adapts its actions based on new data or changes in the environment, ensuring it remains on track toward its goal while optimizing its approach in real-time.
3. Components of AI agents
AI agents are built using an architecture designed to perceive information from environments, process that data, make decisions, and take actions to achieve their goals. At the core of an AI agent’s architecture is its agent function, which guides its behavior based on its objectives and the information it gathers from various sources. The architecture typically includes several key components: data acquisition tools, knowledge bases, and action modules.
-
Data Acquisition Tools: This might include APIs, web scraping tools, or integration mechanisms with other software platforms. For instance, an AI agent may access data from online databases, pull information from web pages, or interact with cloud-based applications to obtain the information it needs to make decisions.
-
Knowledge Base: The knowledge base is a repository where the agent stores information, past experiences, or learned patterns. In AI agents, this can include access to databases, pre-trained models, or historical logs of interactions. Advanced agents update their knowledge base over time, using learning algorithms to refine their behavior and improve decision-making by learning from past tasks and outcomes.
-
Action Modules: These modules enable the agent to perform actions in its environment. This might involve task automation like sending emails, workflow automation, updating records in databases, or interacting with other software applications via APIs. Action modules allow the agent to execute complex sequences of tasks autonomously, moving toward its goals based on the information processed and the plan it has formulated.
These components work together to create the architecture of AI agents, enabling them to autonomously interact within virtual environments, make informed decisions, and complete tasks with minimal human intervention. By continually gathering information, processing it, and executing actions, AI agents can efficiently manage workflows, automate processes, and achieve complex objectives across various software platforms.
4. Types of AI Agents
AI agents can be categorized based on the complexity of their architecture and their ability to respond to and interact with their environment. Each type is designed to handle tasks differently, from simple, immediate responses to complex, goal-oriented behavior that evolves over time. Here’s an overview of the main types of AI agents:
- Simple Reflex Agents: These agents respond directly to specific inputs using predefined rules, without storing past information, making them suitable for straightforward, immediate-response tasks like basic spam filtering.
- Model-Based Reflex Agents: Building on simple reflex agents, these agents use stored information or a model of the environment to make decisions based on both current conditions and past experiences, allowing for more context-aware actions.
- Goal-Based Agents: These agents work towards specific objectives, evaluating different actions and planning steps to achieve a defined goal, such as finding the shortest route in navigation systems.
- Utility-Based Agents: These agents assess multiple options based on a utility function (e.g., speed, efficiency) to choose the most optimal action, ideal for scenarios like financial trading where several outcomes are possible.
- Learning Agents: The most advanced type, learning agents evolve their behavior over time using feedback from their actions, enabling them to adapt and improve in dynamic, changing environments like spam detection systems.
Each type of AI agent builds upon the previous one, increasing in complexity and capability. This range of types allows developers to choose the most appropriate architecture based on the specific needs of a task, whether it's simple, routine work or complex, goal-driven behavior requiring adaptability and learning. When building an AI agent it’s a good idea to keep these different agent types in mind, balancing your needed outcome with the build complexity to achieve the optimal result for your purposes.
5. AI Agent Use Cases
AI agents are versatile tools that can automate and enhance a variety of business processes across industries. Below, we highlight some AI agent uses cases, including customer service automation, and document and contract management. We also explore how you can use AI agents for enterprise applications, and also how you can integrate them with existing business tools like CRMs.
Customer Support and Engagement
AI agents can significantly improve customer support by automating and optimizing interactions with clients. Learn how to Automate your call center quality assessment and Build a HIPAA-compliant AI chatbot to provide secure, consistent customer support in healthcare and other regulated industries. You can also Build enterprise-grade custom AI assistants to handle customer inquiries and provide personalized responses, enhancing client engagement and satisfaction.
Document and Contract Management
AI agents can streamline document analysis and contract management, saving time and reducing human error. Explore how to Automate contract redlining with AI or Analyze tender documents with AI to automate document reviews and make faster decisions. Other powerful solutions include Extracting insights from documents with AI and enabling users to Chat with any document or PDF for quick information retrieval and interaction.
Enterprise AI Assistants and Automation
Enterprise settings benefit from custom AI agents designed to automate complex workflows. Discover how to Build an AI assistant for RFP responses or Automate KYC and due diligence with AI to ensure compliance and efficiency in finance and operations. You can also Chat with Financial Reports to extract insights from a company’s detailed financial reporting with minimal time. These tailored solutions can transform how businesses manage and extract value from data.
Integrations with CRM and Business Tools
AI agents can enhance business productivity by integrating with existing software solutions. Learn how to Chat with your Salesforce CRM, or Chat with Microsoft SharePoint, to access and analyze data directly within these platforms, helping teams make informed decisions faster and with greater accuracy.
6. Build AI Agents with Stack AI
Stack AI makes building AI agents accessible to non-developers, providing a no-code platform where users can create and deploy agents without needing to write complex code. By offering a range of pre-built templates, Stack AI allows users to quickly set up AI agents for common use cases, including customer service automation and workflow automation, significantly reducing the time and effort typically required for development. These templates are designed to simplify the process, ensuring that users with minimal technical experience can still build functional and effective AI solutions.
One of Stack AI’s powerful features is its ability to connect AI models with tools and services. With these integrations, you can empower your AI agents to retrieve information from Google searches, database lookups, custom API calls, cloud storage, and more. Your AI agents can also perform actions like sending emails, or updating Notion or Airtable. Stack AI also connects to Zapier and Make.com, connecting you to thousands of other applications. Additionally, Stack AI is continuously evolving; function calls will soon be supported directly within the platform, further expanding its functionality. This feature will allow users to set up and manage complex workflows, enhancing the flexibility and power of the agents they create.
Stack AI also makes it easy to connect and chain large language models (LLMs), allowing users to design agents that can carry out multi-step processes autonomously. Users can also prompt LLMs directly in Stack AI, giving clear instructions for each step of the workflow. By chaining LLMs together and configuring them to interact with external plugins like APIs, users can build sophisticated AI agents capable of executing complex tasks and responding intelligently to various scenarios!
7. AI Agent Q&As
The world of AI is rapidly evolving. Even for those of us who spend our time working and exploring new developments, it can be difficult to keep up with the current state of AI. With this in mind, here are some highlights of common questions related to AI agents.
Can AI agents work without human intervention?
Yes, AI agents are designed to operate autonomously once they are set up with a specific goal or task. They can analyze data, make decisions, and execute actions independently, adapting in real-time. While human oversight may be beneficial for complex or high-stakes tasks, AI agents are capable of handling many processes without continuous human input.
What are some common business applications of AI agents?
While AI agents are a new technology, they are already being implemented into business processes across various industries. Common applications of AI agents include customer service automation, where agents manage interactions and respond to inquiries, and document processing tasks like data extraction and analysis. They are also used in workflow automation, sales follow-ups, supply chain management, and financial analysis to streamline operations and improve efficiency.
Do AI agents require significant technical expertise to implement?
Not necessarily. Modern platforms, especially no-code or low-code solutions like Stack AI, have made implementing AI agents more accessible to non-technical users. While more complex and cutting-edge AI agents may require technical expertise for customization and integration, pre-built templates and user-friendly interfaces allow businesses to set up and deploy basic AI agents with minimal technical knowledge.
How do AI agents integrate with existing software and tools?
AI agents can integrate with existing software and tools through APIs, plugins, and connectors. This allows the agents to access and interact with existing software including CRMs, databases, cloud storage services, and other tools. Modern AI platforms are built to support seamless integration, enabling businesses to incorporate AI agents into their current tech stack without making major changes to their infrastructure.
How do AI agents differ from traditional automation software?
Traditional automation software follows fixed rules and pre-programmed instructions, handling repetitive tasks without flexibility. In contrast, AI agents are more adaptive and goal-oriented, able to make decisions based on data and learn from their experiences. This enables them to handle more complex, dynamic tasks and respond to changes in their environment, making them more versatile and intelligent than traditional automation tools.