Agents

• Pradeep Aswal
• April 18, 2025

Agents usually mean a person who does work on behalf of someone else. To put it simply, an agent is someone who represents someone else and does tasks on his behalf. When we appoint an agent, we guide them thoroughly to do the tasks on our behalf. Now, imagine teaching a machine to do the exact things without any human intervention. Won’t it be like having your own JARVIS? But is it even possible?

Agents are actually more common than you think. AI agents are becoming a key part of Fortune 500 companies. In fact, 90% of companies see better workflow with the power of generative AI agents. And if we take Mark Zuckerberg’s words, “I think we’re going to live in a world where there are going to be hundreds of millions or billions of different AI agents eventually, probably more AI agents than there are people in the world.”

In this article, we will discuss all there’s to know about AI agents. Read ahead to know what AI agents are, how they work, what their features and challenges are and how you can build an AI agent all by yourself. Here’s what to expect in the upcoming sections:

• What are AI Agents?
• How AI Agents Work
• Types of AI Agents
• Features of AI Agents
• Multi-Agent Collaboration
• AI Agents in Autonomous Workflows
• Agent Memory and Tool Use
• How to Build AI Agents for Beginners?
• How to Make Money with AI Agents?
• Agents Marketplace
• Agentic vs Non-Agentic AI Chatbots
• AI Agents vs AI Models
• AI Agents vs Generative AI
• Benefits of AI Agents in the Enterprise
• Use Cases of AI Agents
• Top AI Agents of 2025
• Challenges and Risks of Implementing AI Agents
• Legal and Ethical Considerations of AI Agents
• Future of AI Agents
• Conclusion

What are AI Agents?

AI agents are autonomous systems that can design their workflow and perform tasks without anyone repeatedly asking them to do so. These agents can understand a user query, process it, and do what is needed using the available tools to perform what the user needs. If we talk about agentic meaning, it refers to the capacity or tendency to act independently, make choices and exert control over one’s own life or environment. It is widely used in psychology to refer to a person who has full control of his own life and environment.

So, to sum agentic AI definition: AI agents are intelligence systems that can learn and execute tasks on behalf of a person or a system, without any human intervention. 

Just like human agents, artificial intelligence agents just need a goal or objective to perform. Once the agents have it, they work proactively to create an AI agentic workflow that can perform a wide range of tasks from simply sending an email to addressing open-ended, complex problems like coding and handling enterprise solutions. 

They show reasoning, planning and memory and have a level of autonomy to make decisions, learn and adapt. If you are a Marvel fan, then you must know JARVIS who did everything for Iron Man from suiting him up to helping him make war strategies.

Bill Gates talking about AI Agents – 8 YEARS AGO! pic.twitter.com/BE4y5MTx7r

— Denny ✈️ Network School (@dennythedev) April 7, 2025

How Do AI Agents Work?

Now, let’s understand how these AI agents work. Understanding the agent architecture might sound complex, but it’s divided into 3 simple steps. AI agents work by:

• Planning and setting up a goal
• Deciding the workflow and using the necessary tools
• Learning and improving

Let’s understand each step of an AI agentic workflow:

Step 1: Planning and Setting Up a Goal

The first thing the AI agents do is, planning what exactly they have to do. They don’t decide on their own. We humans must give the agents an end goal or objective that they need to fulfill. You might consider this process as giving AI agents their “reason for life.” 

To help the AI agent set up its goal, you can be any one of the following:

• Developers create and train the agent.
• Deployment teams, who put the agent into use and give people access to it.
• Users (like me and many of you) who give the agent goals and specify what tools it can use.

Once our agent has the goal, it divides it into small steps. This step is called task decomposition. 

Step 2: Deciding Workflow and Using Tools

In step 2, agents decide the AI agentic workflow and use necessary tools to perform each of the small steps that it divides the task into. AI agents are not encyclopedias, they don’t know everything that’s out there in the world. And thankfully, they don’t even act like a know-it-all.

So, what they do is, with the task in hand, agents use many tools to fill in the gaps in their knowledge. They use web searches, APIs, external datasets and even talk to other AI agents to determine what exactly needs to be done to achieve the goal. In between all these, they use agentic reasoning to come up with valid and reasonable solutions to your problems.

Let’s break this step into a simple example. Suppose you are planning a hiking trip to Australia next week and your agentic AI system is helping you with that. It extracts the past weather trends during the season of your visit from external systems, if needed, takes help from another virtual travel agent (also built on AI) that has updated data about the best hiking conditions, combines what it found out and gives you the best plan to enjoy hiking into Aussie mountains.

Step 3: Learning and Developing 

In the final step, the agents learn and develop. Such AI agentic systems use feedback mechanisms that use other agents or human-in-the-loop (HITL). Let’s get back to our hiking trip to Australia. Once the agentic AI system has given you the data, you can give it feedback like “That was a good job” or “No, I needed to know what to carry to hike in Aussie weather.” 

Humans might get frustrated with repetitive feedback and revisions but AI agents don’t. They store the feedback in their systems and don’t repeat the same mistake in its future responses. Other agents that worked in the complex workflows can also give feedback and the whole multi-agent system learns together to completely let the human relax. This whole process is known as iterative refinement. 

@satyanadella says we’ve entered the era of autonomous AI agents, where even CEOs can start “vibe coding.” This shift could redefine how we approach AI and innovation.@Microsoft @BillGates #microsoft #billgates #vibecoding #ai pic.twitter.com/g2dFaoxuJn

— LetsB_uildTheFut_re (@ReLetsb45993) April 6, 2025

If you think you need extensive coding knowledge to understand AI agents and how they work, then you are mistaken. With the Blockchain Council’s Certified Agentic AI Expert program, you can step into the world of agents with no prior coding experience.

Classes of Intelligent Agents

Now that we know the agentic AI definition and how it works, let’s understand the classes of intelligent AI agents. To understand the types of agent AI, we can divide it into one of the two major classes:

• Russell and Norvig’s Classification
• Weiss’s Classification

Russell and Norvig’s Classification

Talking about the first classification, in 1995, scientists Stuart J. Russell and Peter Norvig discussed the idea of intelligent agents in their book Artificial Intelligence: A Modern Approach. They classified agentic AI in 5 major departments:

• Simple reflex agents
• Model-based reflex agents
• Goal-based agents
• Utility-based agents
• Learning agents

Let’s discuss each of them.

Simple reflex agents

Simple reflex agents are the most basic type of AI agents. They work only on the current input and are unable to remember anything that happened in the past. To put simply, they work on a condition-based method that works according to the principle: If A happens then you do B. Suppose you have a heater at your home that automatically turns itself on only when the temperature drops below a certain point. That heater can be considered as a simple reflex agent. Its mechanism depends only on a specific drop in temperature.

Model-based reflex agents

Next we have the mode-based reflex agents. These are a bit more complex than simple reflex agents. They can remember some of the past interactions and store them into their internal memory. Afterwards, they work according to the current input, as well as the memory that they hold within. Suppose you have a smart vacuum cleaner. If it senses dirt, it cleans. Now, if the vacuum cleaner can actually remember that I have a layout of the house and I have just cleaned the kitchen so I am going to clean the rest of the rooms now. What happens here is the vacuum cleaner remembers its past interaction, which in this case is cleaning the kitchen and simultaneously works on the current input, which is, to clean where it might be dirty.

Goal-based agents

Moving forward, goal-based agentic AI are agents that try to achieve a clear goal. Goal-based AI agents think on their own to decide a way to achieve the goal. You might have seen goal-based AI agents around you if you have come across a self-driving car like Waymo. You give a destination to the agentive AI of the car and it automatically drives you there while maintaining all the traffic rules, avoiding obstacles and navigating all by itself.

Utility-based agents

Now, utility-based AI agents are basically goal-based AI agents but a bit smarter. Suppose you have to go from point A to point B and there are two different ways to reach there. While a goal-based agent will focus on just getting you from point A to B, a utility-based agentive AI model will think of its own and decide which path is actually the best to go from A to B. These agents compare different actions and their consequences and choose the best way to complete a task. So, if we take the Waymo car from our previous example and think of its agentic AI as a utility-based one, then it will take you to your destination while calculating real-time which road will get you there faster and safely in terms of signals, traffic and other aspects.

Learning agents

Finally, we have the most advanced form of agentic AI, learning models. A learning agent learns and improves on its own. These agent AI models have a learning element that helps them learn, a performance element that helps them perform according to their knowledge, a critique element to judge the performance of the agent and a problem generator that gives them various problems to develop with time. If you have played chess on platforms like chess.com then you might have noticed how the AI agent there learns your moves and develops itself to make the game harder to win against it.

Weiss’s Classification

Now let’s talk about Weiss’s classification of AI agents. ​Gerhard Weiss, in his book Multiagent Systems, has classified agentic AI systems into 4 major types. According to this 2013 classification, agentive AI systems can be:

• Logic-Based Agents
• Reactive Agents
• Belief-Desire-Intention (BDI) Agents
• Agents with Layered Architectures

Let’s understand what each of them is.

Logic-Based Agents

These agents work with logical reasoning. You give some facts and rules to these agent AI systems and they figure things out according to those inputs. Suppose you have a smart assistant that can understand when it’s raining. So, if it was a logic-based agent, then it would ask you to carry an umbrella if it’s raining or the forecast says it might rain.

Reactive Agents

Next we have reactive agents that just react without thinking to and fro. These agentic AI systems don’t remember its past interactions or inputs. They just react if something happens. You must have noticed such agents around you if you have seen the automatic doors in stores that sense your presence and automatically open. 

Belief-Desire-Intention (BDI) Agents

Now, Belief-Desire-Intention (BDI) agents take things to a higher level. They work on a belief, have a desire and intention and work accordingly. Let’s take the example of the robot vacuum cleaner that we talked about while discussing utility-based AI agents. Suppose it has a belief that the lounge is dirty and the kitchen is clean. It has a desire to make the whole house clean, including the lounge. Now, its intention is to clean the lounge so that it can achieve its ultimate goal of a clean house. So  Belief-Desire-Intention (BDI) agents have a fact that they think is true, consider what they want to achieve and then figure out what they must do to achieve it.

Layered Architectures

Finally, we can think of an agentive AI system that divides its tasks into different layers. Delivery robots are quite popular in countries like China and Japan. You might have watched those reels or TikTok videos where delivery robots deliver food to rooms by getting into an elevator, identifying the room number and safely delivering the food to the customer, all by itself. Usually, such robots are divided into 2-3 layers. The bottom layer is reactive agents; if someone suddenly appears in front of the robot, it doesn’t think much, it just reacts to go around that person. 

The middle layer is goal-based. It knows where the stairs, hallway, rooms, elevators are. This layer helps the robot to enter and get out of the elevator, enter rooms, etc. It does the necessary steps to deliver the food to the customer. Finally, we have the top layer that acts like the brain of the robot. It thinks and plans the route the robot ultimately takes to deliver the food.

Hierarchical Agents

Hierarchical agents are agentic AI systems that have multiple in-built AI agents, each for a specific task. These show layered architecture that we just talked about. It means that in hierarchical agents, 2-3 agentive AI are layered where the lower level coordinates with the higher levels and vice-versa. 

 Features of AI Agents

Multi-Agent Collaboration (Swarm Intelligence)

We talked about the types of AI agents and how they work. Now, you might be thinking, what if there’s a system where we can make multiple agents work together. Won’t that be more efficient? 

Multi-agent collaboration refers to an environment or a system where individual agents work together in harmony. Now what’s the connection of swarm intelligence with multi-agent systems or MAS?

To understand that, we have to understand what swarms actually are. A swarm is a group of individuals (in this case agents) who act together. We see swarms around us everyday. Think of the group of ants working together. You must have noticed how disciplined and efficient a group of ants can be. Such a group is called a swarm. 

Now swarm intelligence is the collective intelligence of the group. Suppose one ant found food somewhere and it signals the rest of its group that hey we have food here. Then the ants interact with each other and the environment to find out the shortest route to get the food. This collective effort, that ultimately results in something quite smart, is known as swarm intelligence. 

In the context of AI agents, individual intelligent agents work together to create a multi-agent system using their swarm intelligence. Each agent has its own designated role but they communicate with each other to collaborate and co-exist. Let’s go through some key cases of multi-agent systems at work to solidify your understanding:

Examples of Multi-Agent Systems

• Financial Analysis at Moody’s

Moody’s is a well-known financial services company. In its October 2024 report, the company announced that it has developed a system of 35 AI agents to enhance its research capabilities. These agents autonomously perform tasks such as industry comparisons and analysis of Securities and Exchange Commission filings. The company assigns specific roles and personalities to each agent to create a multi-agent system that can handle complex financial analyses more efficiently than human researchers alone. 

• Customer Service Automation at Cosentino

For our next example, let’s take Cosentino, a Spanish manufacturer of countertop surfaces.  The company has implemented a “digital workforce” comprising AI agents to manage customer service tasks. These agents have taken over responsibilities previously handled by human staff, such as clearing customer orders. This has allowed the human employees to focus on more strategic roles. 

• Autonomous Vehicle Coordination

We can also see the example of multi-agent collaboration in autonomous vehicles like Waymo. In such vehicles, multiple AI agents are employed within vehicles to manage tasks like navigation, collision avoidance and traffic management. Each vehicle operates as an independent agent, communicating with others to optimize traffic flow and enhance safety. This multi-agent approach allows for decentralized decision-making, crucial for real-time responses in dynamic driving environments. 

• AI Agents in Video Gaming

In the gaming industry, AI agents are used to create intelligent non-player characters (NPCs) that can adapt to player behavior. They provide a more challenging and immersive experience. For example, AI agents in games like “StarCraft II” have been developed to play at a professional level, demonstrating advanced strategies and real-time decision-making capabilities.

• Smart Energy Management

MAS are also utilized to manage energy distribution in microgrids, where each agent represents a component like a solar panel, battery storage or consumer appliance. Agents negotiate energy production, storage and consumption to optimize efficiency and reliability of the power supply.

AI Agents in Autonomous Workflows

MAS bring us to AI agents in autonomous workflows. Agentic AI systems work together in autonomous workflows to achieve individual or collective goals. These autonomous workflows are often controlled by a centralized, decentralized or hybrid authority. The knowledge base of such systems is autonomous as well where the agents learn on their own. And to understand the core principle of such workflows, it’s important to understand the components of AI agents.

Components of AI Agents

Agent Memory and Tools Use

We have covered the basics of AI agents. In the following sections, we will learn how to build an AI agent for beginners. Before that, you must be clear about two core concepts: Agent memory and tools.

Agent Memory

Agent memory refers to the agent’s ability to remember past interactions, data, or events over time. It allows the agent to act more intelligently by recalling what it has previously encountered or done.

There are different types of memory:

• Short-term memory: Temporary storage of information from the current session or task.
• Long-term memory: Retained knowledge across tasks or sessions like remembering who you are or what goals were set earlier.
• Episodic memory: Storing detailed “episodes” of past experiences or interactions.
• Semantic memory: Storing factual information, concepts and meanings.

Tool Use

Tool use in the context of agentic AI means the agent can use external tools or services to complete tasks it can’t handle on its own.

For example the agents can take help from external tools or services for:

• Searching the web
• Running code
• Accessing a calculator
• Using a database
• Calling an API

An agent with tool use doesn’t need to know everything, it just needs to know how to find or compute it. 

How to Build AI Agents for Beginners?

Now that we know what agentic AI is and how it works, you must be intrigued to learn how to build your own AI agent. While we can’t teach you creating JARVIS, in the following section, we will teach you how to build an AI agent for beginners using:

1: OpenAI

2: Make.com

3: n8n

4: Zapier

5: Google Cloud AI

Let’s start building some high-performance AI agents.

How to Build Agents Using OpenAI?

OpenAI doesn’t need a separate introduction. ChatGPT is a household name these days. To make things more interesting, on March 11, OpenAI announced two new tools especially to build AI agents: Responses API and the Agents SDK. While it requires a basic knowledge of coding, we will break the process down in the simplest possible way:

• Set Up Your Environment

Go to OpenAI Playground and install the SDK using pip:​

pip install openai-agents

Make sure to obtain the API keys. For that, you need to register on OpenAI’s platform, get a subscription and acquire the necessary API keys for authentication.​ 

• Initialize the Agent

The next step is to import required libraries. You can import the Agent class from the SDK:​

from openai_agents import Agent

Now it’s time to create the Agent Instance or a specific, running version of the AI agent. In this step you have to define the agent’s behavior and capabilities. Use the following code:​

my_agent = Agent(name=”MyAI”, description=”An AI agent for task automation”)

• Define Tools and Functions

Next, it’s time to give your agent some superpowers. You can equip it with tools like web search or file handling using the following code:

my_agent.add_tool(“web_search”)

You can also create custom functions the agent can perform using the following code:​

def fetch_data():

    # Function implementation

my_agent.add_function(fetch_data)

• Set Up Guardrails and Observability

  • Implement Safety Checks: Configure guardrails to make sure your agent operates within defined parameters, safely and efficiently.
  • Enable Tracing: Utilize observability tools to monitor and debug the agent’s actions.​ OpenAI offers free APIs like the Moderation API to filter unsafe content automatically

• Deploy and Test the Agent

  • Run the Agent: Deploy the agent in your desired environment.​
  • Monitor Performance: Use the observability features to track the Orchestration. Orchestration 

If you want a more detailed guide or want to build an AI agentic system that performs more complex tasks, check out this GitHub coding template to build an AI agent contract with OpenAI.

But before building AI agents on OpenAI, you must keep some challenges in mind that we personally faced while building agents on the platform:

• Using OpenAI API costs $25–50 per 1k queries. Alternatives like Azure Search actually give better service at a lower price. If you are running complex AI agents then you may have to pay $2,000–$20,000 per user per month.
• Agents SDK so far seems just a bit updated version of Assistants SDK but it still needs a lot of updates to function smoothly. For the uninitiated, Assistants SDK had a lot of performance issues like bugs, lagging interface, etc.

III. It will be hard to plug-in any other LLM into an AI agent created using OpenAI stack. The SDK, including the Search, Vectors and the new Response API are tightly bundled with OpenAI’s Stack.

• Compared to Manus, OpenAI’s agents are underwhelming. It might feel like a DIY kit and seem like OpenAI is relying on the users to build something like Manus by themselves.

How to Create Agents with Make.com?

Make.com is one of the most powerful platforms that enables users to automate workflows and integrate various services without extensive coding knowledge. We got our hands on this one as well to present a transparent and unbiased review to you. We used Make.com to automate the publication of existing articles on LinkedIn as social posts. Let’s quickly run through the steps:

• Create a Scenario:

  • Log In to Make.com: Access your Make.com account and start a new scenario.​ 
  • Add a Webhook Module: Set up a webhook to receive data from external sources.​ For our case, we started with a Google Sheet module where we added the links of existing blogs from our website and its topic names in different rows and columns. The more organized your external source is, the more it’s going to be easier for the agent to achieve specific goals.

• Integrate OpenAI:

  • Add the OpenAI Module: Incorporate OpenAI’s API to process data and generate responses.​ This step was important for us as we set the second module to create a caption for the existing article that we put in the Google Sheet.
  • Configure API Key: Input your OpenAI API key to authenticate requests. ​So if we run just this step and the previous one, the agent extracted the article topic and link from the Google Sheet, passed it through OpenAI to generate a summary or caption that it will post on LinkedIn in the following steps.

You can also use other tools if you don’t need OpenAI. And if you are using OpenAI, make sure your prompt explains exactly how you want the response to be.

• Design the Workflow:

  • Define Triggers and Actions: Specify what events will trigger the agent and what actions it should perform.​ For our case, we added a LinkedIn module and set it up for the profile we wanted the posts to go from.
  • Set Up Data Flow: Ensure data moves smoothly between modules for accurate processing. Make.com offers in-built AI helpers that can help you if you are stuck to create the workflow or if one module is showing some errors. 

• Test and Deploy:

  • Run the Scenario: Once you are satisfied with your workflow, test it to ensure it operates as expected.​ But keep in mind, if you are using OpenAI API, then each test run will cost you a few cents.
  • Monitor and Optimize: Use Make.com’s monitoring tools to track performance and make improvements.

What we loved about Make.com was it allows even non-coders to build AI agents with simple drag and drop features. However, it’s laggy, not always correct and above all, slow at processing complex tasks. While it’s amazing for non-coders, it will certainly touch a nerve or two for experienced developers looking for a smoother and faster overall processing.

How to Create Agents with n8n?

If we are talking about building high-performance AI agents for beginners, we can’t forget n8n. n8n is an open-source workflow automation tool that allows users to connect various services and APIs to create complex automations. If you’ve ever used Automate by LlamaLab on Android, n8n will give you serious déjà vu. Here are the steps to build an AI agent with n8n:

• Set Up n8n:

  • Install n8n: Follow the installation guide on n8n’s official website to set up the platform.
  • Access the Editor: Open the n8n editor to start creating workflows.​

• Add Trigger Nodes:

  • Select a Trigger: Choose a trigger node that will initiate the workflow, such as a webhook or a scheduled event.​

• Integrate AI Capabilities:

  • Add OpenAI Node: Incorporate OpenAI’s API to enable the agent to process and generate data.​
  • Configure Credentials: Input your OpenAI API key to authenticate the connection.​

• Design the Workflow:

  • Add Function Nodes: Create custom functions to define the agent’s behavior and actions.​
  • Connect Nodes: Link the nodes to establish the flow of data and operations.​

• Test and Deploy:

  • Execute the Workflow: Run the workflow to test its functionality.​
  • Monitor and Refine: Use n8n’s logging and monitoring features to track performance and make necessary adjustments.

Now what’s the best use of n8n? Honestly, if something is repetitive and can be automated, chances are we’ve already got it running in n8n. Let us break it down further for you:

• Suppose you have a few SaaS tools that don’t really talk to each other. With n8n, you can get alerts flowing into Slack and MS Teams from different systems: tickets, monitoring tools and some internal dashboards. Way better visibility.
  
• If you are in logistics, you can use n8n so that your shipping guy gets automated SMS updates based on the day’s pickup volume, pulled directly from ShipStation and triggered through Twilio.
  
• When a new opportunity is created in your CRM, n8n can pipe that into your project management tool (we recommend ClickUp). Makes handoffs way smoother.
  
• Automated reporting is another great use of n8n. Monthly compliance, customer usage reports, cloud cost summaries, anything that used to be manual, hours-long tasks, can be automated with n8n. It can poll relevant APIs on the 1st of every month and send everything to your shared drive and/or a Slack channel.
  
• You can also build quick internal dashboards using n8n’s webhook + respond nodes. It’s a hacky way to deliver mini webapps without having to spin up full UIs in Retool or something heavier.

How to Build AI Agents Using Zapier AI?

Now, let’s have a quick look at the steps to using AI agents using Zapier AI.

• Access Zapier Agents:

Navigate to Zapier Agents to begin creating your custom AI agent. ​

• Define Your Agent’s Purpose:

Determine the specific tasks and workflows you want your AI agent to handle, such as managing emails, scheduling, or data entry.​

• Set Up Data Sources:

Connect your agent to relevant data sources to ensure it has access to the necessary information. This may include integrating with your CRM, email platform, or project management tools. 

• Configure Behaviors:

Define the actions your agent will perform based on specific triggers. For example, you can set up behaviors for your agent to draft email responses or update records in your CRM.​

• Test Your Agent:

Before deploying, thoroughly test your agent to ensure it performs as expected. Validate that it correctly interprets data and executes the desired actions.​

• Deploy and Monitor:

Once satisfied with its performance, deploy your AI agent. Continuously monitor its activities and make adjustments as needed to optimize its efficiency.

How to Build AI Agents Using Google Cloud AI?

Building an AI agent using Google Cloud’s Vertex AI involves a series of steps to design, configure and deploy a conversational agent tailored to your specific needs. You don’t need to learn coding to build an AI agent on this platform. Here’s a comprehensive guide to help you through the process:​

Set Up Your Google Cloud Environment:

• Create a Google Cloud Project: If you haven’t already, create a new project in the Google Cloud Console. Ensure that billing is enabled for your project.​
• Enable Necessary APIs: Activate the following APIs:​
  • Dialogflow API: Navigate to the Dialogflow API page and click “Enable”.
  • Vertex AI API: Go to the Vertex AI API page and enable it.

Access Vertex AI Agent Builder:

• In the Google Cloud Console, search for “Vertex AI Agent Builder” and select it. This tool allows you to create and manage AI agents.​

Create a New Agent:

• Initiate Agent Creation: Click on “Create a New App” and choose the “Conversational Agent” option.​
• Configure Agent Details:
  
  • Display Name: Provide a name for your agent (e.g., “Travel Assistant”).
  • Region: Select the appropriate region for deployment.
  • Agent Name: Define a unique name for the agent.
  • Goal: Specify the primary objective of the agent (e.g., “Assist users with travel bookings”).
  • Instructions: Provide initial instructions or greetings for the agent to use when interacting with users.

Integrate Data Sources (Optional):

• To enhance your agent’s responses with specific information, you can connect it to external data sources:​
  • Create a Data Store: In the Agent Builder, navigate to “Data Stores” and click “Create”. Choose the data source type (e.g., Cloud Storage) and specify the data location.
  • Attach Data Store to Agent: After creating the data store, link it to your agent to enable it to access and utilize the available information during interactions.

Test Your Agent:

• Use the built-in simulator in the Agent Builder to interact with your agent. This allows you to assess its responses and make necessary adjustments.​

Deploy the Agent:

• Publish the Agent: Once satisfied with its performance, deploy the agent by clicking on the “Publish” button.​
• Integration Options: You can integrate the agent into various platforms, such as embedding it into a website or connecting it with messaging services.​

Monitor and Optimize:

• After deployment, continuously monitor the agent’s interactions and performance. Use the insights gained to refine its responses, update data sources and enhance user experience.

How to Make Money with AI Agents?

If you have followed the steps outlined above, you already built your own AI agent. Now what if we told you you can actually make handsome money from it? AI agents are high in demand. In fact, the agents market is set to touch $47.1 billion by 2030. It’s around $7.4 billion in 2025. But how exactly can you make money with AI agents?

Out of the many ways available, we think the best way is to sell your AI agent to make a fortune. Just go through the following steps to make money with AI agents:

1. Identify the Problem Your AI Agent Solves

Understanding and articulating the specific problem your AI agent addresses is crucial. This clarity will help in targeting the right audience and crafting compelling marketing messages.

Examples of Problems Solved by AI Agents

• Customer Support Automation: AI agents can handle routine customer experience, inquiries, reducing response times and operational costs. For instance, chatbots that answer FAQs or process simple transactions. ​
• Data Entry and Management: Automating repetitive simple tasks like data entry to minimize errors and free up human resources for more complex activities. ​
• Appointment Scheduling: AI agents can manage calendars, schedule meetings and send reminders, streamlining organizational workflows. 

2. Build an Online Portfolio to Showcase Your AI Agent

Creating a professional online presence is essential to demonstrate the capabilities and benefits of your AI agent.

Recommended Platforms for Portfolio Creation

• Canva: Offers user-friendly tools to design and publish portfolios with customizable templates. ​
• Adobe Express: Provides a free online portfolio maker with professional designs to showcase your work effectively. 
• Wix: A versatile website builder with a range of templates suitable for creating detailed portfolios. 

Portfolio Content Suggestions

• Introduction: Briefly describe yourself and your expertise.​
• Problem Statement: Clearly define the problem your AI agent addresses.​
• Solution Overview: Explain how your AI agent provides a solution, highlighting unique features and benefits.
• Demonstrations: Include screenshots, videos, or interactive demos showcasing your AI agent in action.​
• Testimonials: If available, add feedback or case studies from early users or beta testers.​

3. Choose Platforms to Sell Your AI Agent

Selecting the right agents marketplace or platform is vital for reaching potential buyers.

Potential Platforms to Consider

• Sell Your AI: A marketplace that allows users to buy and sell ready-made automation workflows from platforms like Zapier and Make.com. ​
• Agent.ai: A professional network and marketplace for AI agents, enabling discovery, connection and hiring of AI solutions.
• Beam AI: Facilitates building and deploying AI agents, offering integration into various workflows. ​

4. Develop a Marketing Strategy

Effectively promoting your AI agent will enhance visibility and attract potential buyers.

Strategies to Implement

• Content Marketing: Create blog posts, tutorials, or case studies demonstrating the effectiveness and applications of your AI agent.​
• Social Media Promotion: Utilize platforms like LinkedIn, Twitter and Reddit to share updates, engage with communities interested in AI solutions and participate in relevant discussions.​
• Networking: Attend industry conferences, webinars and workshops to connect with potential clients and partners.​
• Partnerships: Collaborate with businesses that can benefit from your AI agent, offering them trials or demonstrations.​

5. Set Up a Pricing Model

Determine a pricing strategy that reflects the value of your AI agent while remaining competitive.

Common Pricing Models

• One-Time Purchase: Charge a single fee for lifetime access to the AI agent.​
• Subscription-Based: Implement monthly or annual fees for continued access and support.​
• Freemium Model: Offer basic features for free, with premium features available through a paid plan.​

6. Provide Excellent Customer Support

Offering robust support will enhance user satisfaction and encourage positive word-of-mouth referrals.

Support Strategies

• Documentation: Create comprehensive guides and FAQs to assist users in setup and troubleshooting.​
• Responsive Communication: Ensure timely responses to user inquiries and feedback.​
• Regular Updates: Continuously improve your AI agent based on user feedback and technological advancements.​

7. Gather and Showcase Testimonials

Positive reviews and testimonials build credibility and can significantly influence potential buyers.

Approaches to Collect Testimonials

• Beta Testing: Offer your AI agent to a select group for free or at a discounted rate in exchange for feedback.​
• Follow-Up: Reach out to users after purchase to gather their experiences and suggestions.​
• Incentivize Reviews: Provide small incentives for users who leave detailed testimonials or case studies.

Agents Marketplace

We just talked about how you can make money with agents. In this particular action, we will give you a list of best agents marketplace to buy and sell agentic AI systems. An AI agent marketplace is an online platform where businesses and individuals can discover, purchase, or integrate these AI agents into their operations. These marketplaces offer a variety of AI-driven solutions tailored to different industries and functions, enabling users to enhance efficiency and productivity.​

Notable AI Agent Marketplaces

• Agent.ai: A professional network and marketplace for AI agents, allowing users to discover, connect with and hire AI agents for various tasks. ​
• AI Agents Directory: Provides a comprehensive directory of AI agents and agent frameworks, helping businesses find suitable AI solutions for their needs. 
• Enso: A marketplace featuring AI agents designed by experts to assist small businesses in automating tasks and driving growth. ​
• SwarmZero: Offers a platform for developers to build, deploy and monetize AI agents, along with an Agent Hub where users can access these agents. ​
• Fetch.ai: An open platform providing tools to build and deploy AI applications and services, facilitating the creation of autonomous agents for various tasks.

Agentic vs Non-Agentic AI Chatbots

Learning about how AI agents work, you might be thinking, is it the same as other virtual assistants? Is ChatGPT an AI agent too?

The simple answer is, NO.

The brain of the AI agent is made of a powerful LLM or Large Language Model. But they are better than the traditional ones like the IBM® Granite™ models or early OpenAI models. These models were trained on a lot of data. They understood the user’s query and could answer them promptly. So can we call them agents?

Absolutely not. Regular AI chatbots or virtual assistants respond to a user’s query following a pre-fixed script. If you are a regular user of ChatGPT, you might have noticed the answers it generates uses some specific words, punctuation and grammar patterns.

But AI agentic systems can do beyond generating responses. They can understand the context and meaning behind what we tell them, recognize emotions and even figure out the intention behind our questions or commands. AI agents can make independent decisions, adapt to changing situations and provide more personalized experiences for users. 

AI Agents vs AI Models: Key Differences

Another question that might pop up to our minds: Are AI agents and AI models the same?

While they might sound similar, they are totally different. AI models are systems that are made to do a specific task. Google Lens is a good example of an AI model that can recognize images. Some advanced AI models use techniques like chain-of-thought to break down complex coding problems into smaller steps to help reach the solution. But they don’t function on their own. Nor can they learn from interactions and develop accordingly. 

AI agents, on the other hand, can do everything AI models can and beyond. The main difference between AI agents and AI models is the use of native cognitive architecture for agentic reasoning in the former. 

AI Agents vs Generative AI

Benefits of AI Agents in the Enterprise

In the very beginning of this article we talked about how enterprises can’t wait to get their hands on AI agents. And honestly? It makes a ton of sense. The upside of agents in business processes is wild if you’ve got even halfway decent implementation.

Cost Savings

One of the most immediate benefits of AI agents is cost reduction. By automating routine work, companies no longer need large teams to handle repetitive tasks. This resource allocation leads to impressive savings. According to a Litslink study, companies leveraging AI agents enjoy a 55% increase in efficiency and a 35% reduction in operational costs.

Better Decision-Making

Good decisions depend on timely, accurate data—and that’s precisely what AI agents deliver. They analyze complex real-time data and offer actionable insights, enabling leaders to react quickly to market shifts and devise effective strategies. Forbes emphasizes that AI-powered insights significantly enhance business decision-making processes.

Happier Customers

AI agents bring a personal touch to customer interactions—fast, customized responses that boost customer happiness and loyalty. Customers today expect personalized attention and immediate solutions, exactly what AI agents deliver effortlessly. A Zendesk report highlights that AI significantly enhances customer service by offering tailored, around-the-clock support.

Use Cases of AI Agents

Till now you have already learned how effective agents can be. Let’s understand some AI agents examples in the real world. It goes beyond the enterprises. 

AI Agents in Retail and Consumer Packaged Goods (CPG)

The first one is how AI agents work in operations and customer engagement. A recent survey revealed that 75% of retailers consider AI agents essential for competitiveness, as they automate merchandising, pricing, promotions and sales optimization. For example, AI-driven recommendation systems analyze customer behavior to suggest products, enhancing personalization and boosting sales.​

Beyond customer service, AI agents in retail are used for inventory management and demand forecasting. They analyze sales data to predict stock requirements, ensuring optimal inventory levels and reducing waste. This leads to cost savings and improved supply chain efficiency.​

AI Agents in Financial Services

Financial institutions are increasingly adopting AI agents to enhance efficiency and customer service. Arta Finance, a wealth-management startup, has developed an AI assistant capable of providing investment advice using Gen Z slang, appealing to younger investors . Additionally, Bloomberg’s AI tools can streamline up to 80% of an analyst’s workload by automating research tasks .

AI Agents in Manufacturing

Manufacturers are using AI agents to predict maintenance needs, improve quality control and optimize production schedules. For instance, Microsoft’s Factory Operations Agent assists in diagnosing issues by analyzing vast amounts of factory data, enhancing operational efficiency. These agents contribute to increased productivity and reduced operational costs.​

AI Agents in the Automotive Industry

The automotive sector benefits from AI agents in areas like autonomous driving and customer service. A survey indicated that 70% of consumers would use an AI agent if it could diagnose and address car issues in real time. Companies are integrating AI to enhance vehicle diagnostics and personalize in-car experiences.​

AI Agents in Healthcare and Life Sciences

In healthcare, AI agents assist in drug discovery, patient care and administrative tasks. Isomorphic Labs, an Alphabet subsidiary, raised $600 million to advance its AI-driven drug design engine, aiming to accelerate medical breakthroughs. Additionally, AI agents are used to automate regulatory document generation, streamlining compliance processes.

AI Agents in the Insurance Industry

Insurance companies employ AI agents to automate claims processing, detect fraud and personalize customer interactions. For example, virtual agents can answer queries about policies and provide personalized information such as claim statuses. This automation enhances efficiency and customer satisfaction.​

AI Agents for Business Teams

Service Teams

Service teams utilize AI agents to handle customer inquiries, process requests and provide support. These agents can manage routine tasks, allowing human agents to focus on more complex issues, thereby improving overall service efficiency.​

Sales Teams

Sales teams benefit from AI agents by automating lead generation, scoring and follow-ups. For instance, AI sales assistants can analyze customer data to identify potential leads and recommend personalized sales strategies, enhancing conversion rates.​

Commerce Teams

Commerce teams leverage AI agents for dynamic pricing strategies and personalized marketing. By analyzing market trends and customer behavior, these agents adjust pricing in real-time and tailor promotions to individual preferences, boosting sales and customer loyalty.​

Marketing Teams

Marketing teams employ AI agents to create content, manage campaigns and analyze performance metrics. Tools like Salesforce’s Einstein GPT assist in generating marketing content and automating customer interactions, streamlining marketing efforts.

AI Agents in Software Development

Agentic AI systems make things easier for developers as well. Tools such as GitHub Copilot assist developers by suggesting code snippets, enabling them to focus on more complex aspects of their projects. This integration of AI not only accelerates development but also enhances code quality and consistency. ​

According to Microsoft’s CTO, Kevin Scott, within the next five years, 95% of code will be generated by AI, with minimal line-by-line coding done by humans. This shift emphasizes the importance of human software engineers in the creative and problem-solving aspects of programming. So, will AI replace programmers? No, if you upskill yourself with expert-led certifications like the Certified Agentic AI Developer program. This certification is the best in the market to learn agentic AI and make the most of them. 

Best AI Agents of 2025

Since powerful AI agents have basically exploded this year, figured we’d drop a quick rundown of the ones that actually stand out in 2025. So here are the top examples of AI agents as of 2025:

Manus AI

Developed by the Chinese startup Monica, Manus AI is a fully autonomous agent capable of independently managing complex tasks such as sorting résumés, analyzing stock trends and building websites. Users have praised its groundbreaking potential, although some have noted tendencies to make errors and raised concerns about privacy and data security. ​

Auto-GPT

An open-source AI agent that allows users to create autonomous assistants capable of completing complex tasks. Utilizing advanced language models, it breaks down goals into subtasks. Common use cases include social media content creation, text translation and web design. Users commend its flexibility and the ability to automate multifaceted tasks effectively. ​

Superagent

This platform enables users to build and deploy personal AI assistants similar to ChatGPT. Specializing in web research, it can also answer questions, generate content and automate internal workflows. Superagent has garnered attention for its user-friendly interface and robust functionality, reflecting strong user interest and confidence in its capabilities. ​

AgentGPT

An AI agent that allows users to configure and deploy autonomous AI agents directly in their browsers. It is designed to handle a variety of tasks, from web research to content creation and is praised for its ease of use and accessibility. ​

BabyAGI

A Python script that leverages OpenAI and Pinecone APIs to create tasks based on previous results and a predefined objective. It is designed to emulate an artificial general intelligence (AGI) system, capable of autonomously performing tasks. Users appreciate its innovative approach to task automation. ​

DeepSeek Agent

Designed to perform complex, multi-step tasks autonomously by combining large language models with reasoning, tool use and memory. It serves as a powerful foundation for long-running data analysis workflows and research assistants. ​

Microsoft Copilot

Integrated into the Microsoft 365 ecosystem, Copilot is designed to supercharge productivity and streamline data analysis tasks. Users have noted its effectiveness in enhancing workflow efficiency within Microsoft applications. ​

Amazon Rufus

An AI shopping assistant launched by Amazon in February 2024, Rufus aids customers in product searches and recommendations. Despite early mixed feedback, Amazon anticipates significant financial gains from its implementation.

Google’s Project Astra

Announced on May 14, 2024, Project Astra is Google’s universal AI agent designed as an everyday assistant. It integrates with devices and wearable technology, offering audio and video capabilities for contextual understanding and real-time responses.​

Do Anything Machine

Launched by Garrett Scott on April 11, 2023, this personal AI agent focuses on task management by analyzing and prioritizing tasks based on factors like importance, urgency and deadlines. Users find it instrumental in organizing and automating their daily activities, enhancing productivity.

Challenges and Risks of Implementing AI Agents

Legal and Ethical Considerations of AI Agents

As agentic AI moves from labs into real-world business workflows, legal questions are quickly becoming unavoidable. These systems are no longer just helping with tasks. They’re involved in decisions that affect contracts, finances and even personal rights. The core issue is accountability in systems that are mostly opaque.

Liability

Start with liability. If an AI agent misreads a contract, gives flawed legal recommendations or processes data in ways that violate policy, who is responsible? The company using it, the vendor that built it or the developer who trained it? There is very little case law to answer these questions and businesses using agentic AI in sensitive domains will need to prepare for the legal fallout if something goes wrong.

Data Governance

Data governance is another key concern. Agentic systems that process personal or sensitive data must comply with regulations like the CCPA or GDPR. That includes getting proper consent, being clear about how data is used and protecting it from misuse. Many of these models are trained on massive datasets with unclear origins. That creates legal risk, especially when those agents are used in industries like finance, health or law.

Confidentiality

In legal work, confidentiality is everything. If AI agents are reviewing contracts or client files, there needs to be a guarantee that none of that data is leaked or stored in unsafe environments. Proprietary models with closed architectures make that assurance hard to prove. Without visibility into how the system works or where data goes, there’s a trust gap that open source models are better positioned to fill.

Bias

Bias and discrimination are also front and center. If an agent is used in hiring, lending or access to services, any unfair patterns in its logic could lead to violations of anti-discrimination laws. Businesses may soon be required to show how their AI makes decisions, whether it was tested for fairness and how it handles edge cases. That’s a big ask when most systems can’t explain their own reasoning.

Intellectual Property

Then there’s intellectual property. If an AI agent is trained on proprietary or copyrighted materials and starts producing outputs that resemble them, there could be questions about ownership and derivative use. Companies using agentic systems to draft legal documents or generate content need to know where that output is really coming from and who owns it.

Transparency

Transparency is becoming a dividing line. Closed source models guard their code, weights and training data as trade secrets. Open source models often publish them. For businesses concerned with risk and compliance, having access to the full picture is not just helpful—it’s necessary. Without transparency, due diligence becomes guesswork.

Future of AI Agents and Best Practices

Prediction 1: Agents teaming up, without needing you there

In 2025, we’re going to see AI agents not just doing solo jobs like helping in sales or support, they’ll start teaming up with each other to handle big, complex stuff. Think building a full marketing campaign or running a sales strategy, stuff that usually needs people from different departments. These agents will work together, adjust on the fly and actually get things done, almost like a team of coworkers that doesn’t need constant human supervision.

Prediction 2: Personal agents for everyone

It won’t just be big companies or devs running fleets of AI agents. Expect to see everyday users running their own personal AI stacks: one agent for scheduling, one for managing finances, another for handling inbox triage, etc. Basically like having a digital entourage, but customized for each person’s life. The barrier to entry is dropping fast.

Prediction 3: New rules for working with AI

As AI becomes more human-like in how it helps out at work, we’ll have to figure out some new social norms. There are going to be real questions, like, are we relying too much on AI? Are we losing that human touch? Sure, AI can make us more productive and help us stay connected, but it could also mean fewer real interactions between people. We’ll need to find the balance and make sure AI is supporting us, not replacing what makes us human.

Prediction 4: Agents with memory (and opinions)

Right now, most agents are kinda goldfish, they forget everything after the task is done. But the next-gen stuff? They’re going to remember past conversations, learn your preferences, and even start pushing back. Like, “hey, based on your past choices, this doesn’t look like a good idea.” Agents with memory = way more useful and way more complex to manage.

Prediction 5: Agents will need credentials

As agents start handling sensitive stuff (payroll, contracts, legal filings), companies will need ways to authenticate them like they would a real employee. Expect to see “agent IDs,” permission systems, audit trails, and security protocols that treat agents like actual actors in the org chart.

Read More- How Blockchain and AI Agents Together Will Reshape the Future

Conclusion

Today we can say there’s an AI for everything. From creating emails to automate the whole process of sending it, agents can make it possible. While it is a powerful tool, the question remains: Can AI take over the world? The answer is no, with a condition. You must match the rapid pace that AI is developing with. Enroll into expert-led programs by the Blockchain Council to learn agentic AI and how you can use it to stand out.