## **What are AI Agents?**

Imagine you are stuck in a heavy traffic jam, and your car's smart device of your car adjusts the temperature just before you start feeling cold, and the device of your car suggests a movie that you will love. These are not smart devices; these are powerful and smart teammates working behind the scenes. These are AI Agents.

AI Agents is a program that observes its surroundings, makes its own decisions, and takes actions to achieve a specific goal automatically. Think of it as a digital helper. We give an adjective to an agent, “get me to the airport” or “keep the house at 70 degrees,” and that agent thinks, determines, and acts to finish those tasks.

What makes AI agents different from an AI assistant or a [Large Language Model (LLM)](https://en.wikipedia.org/wiki/Large_language_model)? The difference is that an AI assistant and a Large Language Model (LLM) like [ChatGPT](https://chatgpt.com/) are mostly reactive; it does not perform tasks on their own, they require a direct prompt to perform tasks. They can generate text, answer questions, or write code, but they do not do anything without being asked. While an AI agent does not require prompts again and again, it can perform multiple tasks without any human intervention. It has the power to act.

There is an amazing analogy, let’s go through it! Think of CHATGPT as a good researcher you can consult for information you want. An AI Agent is your project manager; you give the manager your goal and things you want to achieve for your business. They work towards it and hire the researcher, coordinate with different departments and manage resources, and look after your project automatically. What we have defined is not enough to describe an ai agent; its capability of independently executing actions, making decisions, and changing to achieve the goal is what makes it special.

We are going to take you through every autonomous system, meaning agentic AI. We will explore the basic building blocks that all agents have in common, so go through this article and see how they are already changing the industries and powering technologies like autonomous driving cars.

## **How AI Agents Work: The Basic Building Blocks**

No matter how simple or complex it is, from a basic thermostat to a high-tech, self-driving car, all AI agents are built from an initial structure that determines how they can interact with their environment. Very powerful frameworks understand these interactions and break down the agent’s existence into four components. The PEAS model, which means Performance, Environment, Actuators, and Sensors, 

To make this concept understandable, let’s take an example of a non-digital agent: a human baker.

- **Performance:** This takes care of the success for the baker it is making tasty and perfectly cooked cake. 
- **Environment:** The workspace and its conditions. This is the kitchen, with its oven, ingredients, bowls, and other environments of kitchen. 
- **Actuators:&#x20;**&#x54;hese are the tools that we can use to act according to the environment. The baker’s actuators are their hand for mixing ingredients, legs for moving, and voice for asking for help.
- **Sensors:** These are the tools referred to as baker’s eyes to check if the cake is baking properly. Their nose for smelling to know if it is done, and their ears for hearing the oven timer 

An initial AI agent can be as described using this same PEAS framework.

### **The Environment**

The environment is the world of the agent, where it operates. The nature of these environments is an important factor. It directly affects how an agent should be to succeed. These environments can also be physical forms, like a road for an auto-driving car or a warehouse for a logistics robot. And these environments can also be virtual, such as the internet for a trading bot or a company’s customer database for a service agent. 

### **Sensors**

Sensors are the agent's "senses," the components it uses to gather information, or "percepts," from its environment 

- **Physical Examples:** Auto-driving cars use sensors like cameras for checking if someone is around or coming, LiDAR for making 3D maps, radar for detecting the speed of objects, and microphones for emergency sirens.
- **Virtual Examples:** For a chatbot, its sensor is the interface that receives text from a user query. Most of the software agents use API to get the data from external services, or they read documents like PDFs and web pages to get the information.

### **Actuators**

They are like the hand or voice of the agents, the mechanisms they use to take actions and create changes in its environment.

- **Physical Examples:** For a car, its actuators are the steering wheel, the accelerator, and the brakes, and for a robot, its wheels, arms, and grippers.
- **Virtual Examples:** An agent's action can be mostly in the digital form, such as showing text on the screen, sending an email, performing a financial trade, or updating a record in a customer relationship management system.

### **The Agent Program (The "Brain")**

The program we write is the heart of every agent; this is the core logic, the decision-making engine that handles the data from the sensors and makes decisions on which actions it should take by the actuators. 

## **The Main 6 Types of AI Agents: A Journey from Reaction to Reason**

Think of the different types of AI as steps. Each new type of AI was built to be better than the AI before, fixing its problems. This is similar to how newborn babies develop their thinking skills. It started with basic feelings (like knowing how to eat), then came memory, then making plans, and finally, learning and changing based on new experiences. If you understand this step-by-step improvement, it's easier for you to see what each type of AI is all about.

### **Type 1: Simple Reflex Agents (The "If-This-Then-That" Agent)**

This type of AI is like a very simple robot that has no memory and only reacts on instinct. It just pays attention to what is happening right now and follows a simple rule: "if this happens, then do that."

**How It Works**

For example, an automatic heater with this AI feels that the room is cold and immediately turns on because its rule tells it to. It doesn't remember if the room was hot just a minute ago; it only knows that it's cold right now and follows its command.

**Real-World Examples**

- **Thermostats:** It detects the temperature of the current environment of the car and, based on that, it activates the system if the temperature is low, it activates the heater Air conditioner if the temperature is high, it activates the Air conditioner
- **Automatic Doors:** When someone comes towards the door, its motion detector detects the motion around the door and triggers the “open” action. And no one is there it detects no motion it automatically triggers the “close” action.
- **Basic Email Spam Filters:** These agents scan incoming emails for specific keywords or sender characteristics. If an email contains a trigger word like "lottery" or "free money," the agent applies the rule to move it to the spam folder.
- When you get an email, your agents scan that mail for specific keywords. if an email contains A trigger word like “lottery” or “free money,” the agent applies the rule to move it to spam.
- **Vacuum Robot Bumper:** When there is a wall and there is a rule to stop and turn, and the robot's Physical bumper sensor makes contact with a wall, it just does as per the rule, it stops there and takes the turn.

### **Type 2: Model-Based Reflex Agents (The Agent with a Memory)**

This smarter type of AI has a memory, which is an amazing upgrade. It creates a kind of mental map of objects around it by remembering things, which allows it to remember things even when it can't see them now, like a car that is now in the blind spot. It keeps this mental map updated by understanding how the world changes on its own and how its actions affect its situation. Because of this AI can understand what's happening in places it can't see. This ai function is good in critical environments when it can’t see everything at once

**Real-World Examples**

- **Robot Vacuums:** A high-tech robot vacuum cleaner automatically makes the map of your room and remembers which areas this vacuum cleaner has already cleaned.
- **Self-Driving Cars (Object Permanence):** When you are moving in an high way in an auto driving car and a vehicle ahead of your car is now not visible because a big truck has come in between. The agent does not forget its existence, and its internal model tracks the hidden car's predicted position and velocity, allowing it to react safely and anticipate its reappearance.

### **Type 3: Goal-Based Agents (The Strategic Planner)**

This type of AI agent is really smart because it plans everything before it reaches a specific goal. It always asks itself what to do before doing anything or reacting to anything. Like what action can I take, will it help me to reach my final goal. To figure this out it thinks about many different possible paths and picks the one that leads to the best result. 

A great example is a GPS navigation app. You set a destination (the goal), and it plans the entire route. Sometimes, it might guide you onto a longer street, which is weird, but it does this to help you avoid a major traffic jam. This shows the AI is making a choice that might not seem best in the short term to achieve its long-term goal of getting you there efficiently. It's focused on the final destination, not just the immediate next step.

**Real-World Examples**

- **Chess-Playing AI:** Here, the goal for the AI is to checkmate. The agent does not simply react to what opponent’s last move, but rather predicts many moves already, figuring out which moves it can take that can help it to checkmate the opponent
- **Warehouse Logistics Robots:** A robot is given the goal to "retrieve item #A4 from shelf #B7." It then makes the plan the most efficient path through the dynamic warehouse environment that it has already captured into memory to reach that specific location, navigating around obstacles and other moving robots.

### **Type 4: Utility-Based Agents (The Rational Decision-Maker)**

Now this AI is a smart decision maker that always tries to get the best result to achieve its goal. But it does not just focus on the result it is not enough to complete a specific goal. This agent wants to do this task in the best possible way, getting the highest possible “satisfaction”. 

Let's take an example of booking a flight ticket, you would not want any flight that gets you to your destination(the goal). A flight you find might be cheaper, but that will be the slowest and take a longer route. Another fast but expensive AI gives a score to each option to solve this kind of conflicting goals, like speed vs safety, or cost vs quality, then it chooses the best result that leads to the highest overall score. Giving it the best outcome.

**Real-World Examples**

- **Self-Driving Cars (Advanced Decisions):** Here, the goal is to reach the destination, and an agent is continuously balancing multiple factors: maximizing speed, maximizing safety, maximizing passenger comfort, and minimizing fuel consumption and An agent might choose a long route if it is safer and smoother based on the overall score.
- **Financial Trading Algorithms:** The goal here is not just to make money. The agent’s utility function balances the option between high returns and the risk of substantial loss, optimizing for the best possible risk-adjusted return on investment.

### **Type 5: Learning Agents (The Agent That Gets Smarter)**

This is really a most advanced type of AI because this AI can learn things over the period of time from its experiences to improve itself, just like a human. This ai does not work on certain rules; instead, it starts with some basic knowledge and gets better as it practices and gets feedback. It works on a system that runs in a continuous loop first one is **acting part**, which is the main agent that actually does things in the world. Next, there is the **coach part**, which watches the action and gives feedback on whether it was good or bad. After that, the **learning part** takes that feedback from the coach and uses it to improve the main agent's strategy for the future. Finally, there is an **explorer part**, which suggests trying new or unusual actions. This encourages the agent to experiment and discover new, better ways of doing things instead of just getting stuck in a routine.

**Real-World Examples**

- **Recommendation Engines (Netflix, Spotify, Amazon):** The agent recommends a product or a show (action). Based on the user’s history or behavior, watching it, rating it, and ignoring it. Then the learning agent updates its internal model of the user’s preferences, making the recommendations more accurate
- **Advanced Game-Playing AI (DeepMind's AlphaGo):** Now these agents played millions of games against each other. Whenever the agent wins, it is provided with positive feedback, and whenever the agent loses, it is provided with negative feedback; its learning mechanism uses this feedback to continuously improve its performance.
- **Adaptive Customer Service Chatbots:** These agents learn on their own by thousands of user interactions. They identify what type of response leads to successful resolutions and high satisfaction ratings, and which leads to user frustration with humans. Over time, they change their conversational strategies to become more helpful.

### **Type 6: Hierarchical Agents (The Organizational Leader)**

This final AI is not a new type of AI brain; it is a way of making a team of AI work together, handling a huge, complicated job. It is like a company, you break a big task into smaller tasks and assign them to different levels.

At the very top, we have the “CEO agent.” It has the main goal to fulfill, for example, "Manufacture 1,000 cars this week." The CEO agent doesn't do all the work by itself; it creates a strategy and distributes tasks.

The middle there is “Manager agents”; they take orders from the CEO. A manager might be in charge of a specific department, like the assembly line or the painting station, where they handle the lower category and turn the big strategy into more concrete jobs for the workers.

Now, at the bottom of this, you have the “worker agents.” These are the simple AIs that only do one specific physical task. They follow orders from a manager. One worker agent might control a single robot arm that is working on welding a door, while another is working on controlling a paint sprayer. This system allows a team of many simple AIs to work together to complete a big project.

**Real-World Examples**

- **Smart Factories and Manufacturing:** At the very top level, agent already plans how all the resources to used or schedule overall production, mid-level agents handle specific assembly tasks, and low-level agents control the equipment and robots in the factory to start the production.

If you are here for research for your business, what type of Agents do you want to Do not worry, we give free consultation for businesses who are looking for a [Company that builds agent AI to automate business](https://advantailabs.com/services/agentic-ai-workflow-building).

## **Conclusion: The Future is Autonomous and Collaborative**

The way AI has grown, from a simple machine that just reacts to one that can learn and adapt, shows us that the main goal has changed. We have moved on from just building powerful computer "brains" to creating smart "doers" that can see, think, and act in the real world. The difference between a basic thermostat and a self-driving car shows this perfectly—it's like climbing a ladder of intelligence.

The future of AI is all about teamwork. The next big step is to create teams where different types of specialized AIs work together to solve huge problems. For instance, a team might have one AI that plans goals, another that learns from experience, and a third that makes smart trade-offs. These AI teams will be the foundation for future technology, helping to build everything from smart cities and automated businesses to entirely new scientific discoveries. The invisible AIs that are helping us today are going to be the main architects of the world tomorrow.

What Are AI Agents? And 6 Types of AI Agents with Examples

Are you losing customers when your business isn't active? Is your support team spending most of their time answering the same questions again and again? Because of this, many businesses face challenges, which lead to missed sales opportunities and lost potential clients. The solution isn't always hiring more people; it's about working smarter.

That's where a custom AI chatbot comes in. We build intelligent chatbots for our clients using Python and NLP (Natural Language Processing), Python being one of the most powerful and flexible programming languages available in the market. These aren't your average bots that only understand a few words. These bots are brilliant and provide 24/7 support. They also understand what your customers are asking, generate leads, and drive sales, all while your team focuses on more complex problems.

In this blog, we will delve into more detail and outline the step-by-step process of building AI Chatbots for Businesses with Python and NLP. We will keep this simple and clear. In this blog, you will learn why we use Python, how we "teach" a bot to understand human language, and how it will deliver real results for your business.

## **Why Build a Custom Chatbot with Python?**

There are many drag-and-drop chatbot builders on the internet, but most of the time, they come with limitations. For example, a basic, scripted bot always follows the path it's designed to; it won't respond in an expected way if it's asked something it's not designed to do. Most of the time, this can frustrate customers with a "Sorry, I do not understand" message.

A custom chatbot built with Python provides a high level of flexibility and power.

- **Deep Integration:** We connect the chatbot directly to business systems, such as sales CRMs, product inventory, or booking calendars. This allows the bot to perform your preferred tasks, such as scheduling a demo or checking the status of an order.
- **Full Customization:** We build chatbots that specifically fit the industry, which means we tailor their personality, conversational style, and knowledge to our clients' business models.
- **Scalability and Security:** As the business grows, the chatbot will also grow along with it. You will have complete control over the data, which is a very important factor for businesses handling sensitive customer information.

We choose Python because it is very simple to use and has many powerful pre-built tools and libraries, mostly for AI and Natural Language Processing (NLP), which allows us to develop highly effective bots.

## **The "Magic" Explained: How a Chatbot Understands You**

Now, before we deep dive into the "how," let's help you understand the technology that makes a chatbot feel smart and intelligent. This technology is called Natural Language Processing (NLP), and it is a part of AI that provides computers the ability to read, understand, and respond to human language in a more human-like way.

To make it easy to understand, just assume your chatbot is a detective solving a case with every message from a user.

- **The Motive (Intent):** Do you know what the first thing a detective would do? A detective would first try to find out the motive. In the AI Chatbot world, the **Intent** is the user's ultimate goal. For example, a user might say, "Book a flight," "I need a ticket," or "Find a flight to Boston." The words are different, but the intent is the same: Book Flight. NLP helps the chatbot recognize this core goal, no matter how the user phrases it.
- **The Clues (Entities):** Now the detective knows the motive, but what next? Now the detective tries to look for clues. Just like that, for a chatbot, these clues are called **entities**. Entities are important pieces of information a chatbot needs to fulfill the user's request. For example, if a user says, "Book a flight to Boston for tomorrow," the entities are "Boston" (the destination) and "tomorrow" (the date). The bot is trained to extract these key details to take action. Without understanding the intent, the bot can't do anything, and it can't take action without extracting the entities. NLP is the "training" that teaches the bot how to do both.

## **Our Blueprint: Building an AI Chatbot in 6 Steps**

Now you know the basics, so we are going to show you the exact blueprint we follow to build a powerful AI chatbot that solves real business problems.

### **Step 1: Define the Mission**

Before we start building an AI Chatbot, we sit and discuss with our clients to understand the chatbot's mission. Your AI chatbot is nothing without a purpose. We then establish specific, measurable goals, for example:

- Answer the top 15 most common customer questions 24/7.
- Qualify new sales leads by asking about their budget and needs.
- Book appointments directly into the sales team's calendar.
- Smoothly hand off complex or frustrated customers to a human agent.

This "job description" guides every decision we make during the development process.

### **Step 2: Choose the Toolkit**

After knowing the business mission, we choose the best Python libraries that fit to achieve the goals. These are free toolkits that help us develop faster and more effectively.

- **NLTK (**[**Natural Language Toolkit**](https://www.nltk.org/)**):** This is a very important library for initiating basic NLP tasks like breaking sentences into words and recognizing parts of speech. It is like teaching the bots the basic grammar of a language.
- [**ChatterBot**](https://pypi.org/project/ChatterBot/)**:** This is an excellent library for building the "brain" of a chatbot. This library allows the bots to understand meanings and conversations; it gets smarter and more efficient over time as it interacts with more users.

### **Step 3: Design the Conversation Flow**

Next, we act like screenwriters and map out the entire conversation. We design the "happy path" where everything works smoothly, but we also plan for when users ask anything unexpected, get frustrated, or want to speak to a person. This conversational flow ensures the user experience is helpful and intuitive, not robotic or with a dead tone.

### **Step 4: The Build (A Glimpse of the Code)**

This is the point where we bring the plan to life. Many agencies find it hard to build a full-featured chatbot that involves complex code. With our workflow, we manage to get the bot running in Python with straightforward code. Here's a simplified example using the ChatterBot library to show you what it looks like:

```
# First, we import the main ChatBot tool
from chatterbot import ChatBot

# Next, we import the 'trainer' that will teach our bot
from chatterbot.trainers import ChatterBotCorpusTrainer

# We create a new chatbot and give it a name
my_bot = ChatBot('BusinessAssistant')

# We create a trainer for our new bot
trainer = ChatterBotCorpusTrainer(my_bot)

# We train the bot using a pre-built English conversation dataset
trainer.train('chatterbot.corpus.english')

# Now the bot is ready to talk!
# Let's get a response to a greeting

response = my_bot.get_response('Hello!')
print(response)
```

By using this simple code, we create a bot and then train it with some basic English to get a response. This is the initial process upon which we build more intelligent and integrated assistants for our clients.

### **Step 5: Train the Bot with Your Business Knowledge**

A chatbot is only as smart as the data it's trained on. This is the most critical step. To make the chatbot smart and effective for a specific business, we feed the bots with rich data that can help them understand and learn how to provide answers to users if business-related questions are asked. This includes:

- Past customer service chat transcripts.
- The company's full FAQ and knowledge base.
- Product descriptions and manuals.

This is what allows the bot to answer questions about your business, not just general topics. The more relevant the data is, the more helpful the Chatbot will become.

### **Step 6: Test, Refine, and Launch**

Now, the final step before we make the chatbot available to real customers is to put it through rigorous testing. Our team and the client's team act as customers, asking every business-related question we can think of, trying to confuse it, and testing all its features. We try to identify its weak spots, go back to improve the conversation flows by giving it better data to fill knowledge gaps, and only make it available for customers after it consistently passes the testing.

If you are a business owner and want to build the best AI Chatbot for your business, check out our [AI Chatbot Development](https://advantailabs.com/services/generative-ai-product-engineering/ai-chatbot-development) and get a [free consultation](https://advantailabs.com/contact).

## **The Real-World Impact: What a Smart Chatbot Can Do for You**

The true measure of success is the chatbot's impact on the business. For our clients, a well-built chatbot becomes a key driver of growth.

- **Generate More Leads:** A chatbot can engage website visitors 24/7. For one client, MongoDB, implementing a chatbot led to a 70% increase in new leads by asking qualifying questions and scheduling demos, even after the sales team went home.
- **Increase Sales and Bookings:** Chatbots can act as personal shoppers or booking agents. Amtrak increased its bookings by 25% with its chatbot, and those bookings generated 30% more revenue. Retailer Sephora saw an 11% higher conversion rate from customers who used their bot to book appointments.
- **Slash Customer Service Costs:** By handling common, repetitive questions, chatbots free up human agents to focus on complex issues. Amtrak saved $1,000,000 in customer service expenses in a single year, and Vodafone saved over €70 million annually with its bot.

How We Build AI Chatbots for Businesses with Python & NLP: A Step-by-Step Guide

Top 10 AI Agent Ideas For Your Business in 2025

In the AI era, artificial intelligence will change how businesses work with the evolution of AI Agents. AI agents are not just simple tools; they are smart enough to learn on their own once we train them. Gradually, AI agents are becoming more useful and helpful in today's businesses to solve their problems. They can help companies be faster, more productive, and ahead of their competitors. In this article, we will discuss what AI agents are, how fast they are growing, and provide you with 10 of the best AI Agent ideas for your business.

## **What Are AI Agents?**

You might have heard the term "AI Agent" a lot, and to use them well, you also need to know what makes them special and important. They are the best way to automate tasks compared to older methods.

AI Agents are smart computer programs that can use AI to perform tasks for you without you telling them every step. The reason AI Agents are different is that they can think, remember, plan, and learn from what they do. This means they can make decisions on their own. This power comes from new AI technology that can understand text, images, videos, sounds, and computer code.

This is a big chance for businesses to use AI agent automation. It can handle complicated tasks for your business with efficiency. This means you can give them bigger tasks, and your team can focus on more important work.

## **The Global AI Agent Market: A Growing Opportunity**

You must be hearing news about AI and its development on the internet, and you also must be excited to know what the future of AI will be. However, if you just focus on the present, you will also be surprised by the statistics. For the past few years, the AI industry has made massive growth in every way, but one of the fastest-growing markets is for AI Agents. The global market for AI agents is growing very fast. The global market was $5.25 billion in 2024, and in just one year, it has grown to approximately $7.63-$7.84 billion in 2025. Furthermore, it is predicted that the market will exceed $50 billion by 2030.

The above statistics indicate that the market is growing by about 46% every year. That shows that technology is changing rapidly in a big way and that many businesses will be using it. That also means businesses will need to be quicker, smarter, and more focused on customers, and AI agents are the key to doing all that.

## **AI Agent Ideas For Your Business in 2025.**

Knowing about AI agents is important, but if you do not know how to use them, then there is no point in it, so just for that we have listed the top 10 great AI Agents Ideas for how your business can use them in 2025

### **(1) Super-Personalized Customer Support Agents**

- **What it is:&#x20;**&#x41;I agents that can give 24/7 customer support, which is smart and helpful. These AI agents know a customer's history and can offer real solutions, not just simple answers.
- **What it uses:&#x20;**&#x53;mart AI for natural conversations, memory to remember past conversations with the users, and the ability to understand customer feelings and what they are saying.
- **Benefits:&#x20;**&#x48;appier customers, shorter wait times for customers, and lower costs because the agents can handle more questions without the help of a person.
- **The 2025 Edge:&#x20;**&#x54;hese agents will guess what customers need before they even ask, acting like a personal helper and a professional.

### **(2) Smart Sales and Lead Agents**

- **What it is:&#x20;**&#x41;n AI agent that finds new customers, sends them personalized messages, and sets up meetings on its own.
- **What it uses:&#x20;**&#x53;mart data analysis to find the best leads for the specific industry, AI to write personalized emails, and automation to manage follow-ups.
- **Benefits:&#x20;**&#x59;our sales team can focus on closing deals instead of looking for new customers and wasting time on getting leads. This leads to more sales and no missed chances.
- **The 2025 Edge:** These agents will have real conversations with potential customers to see if they are a good fit, acting as a first filter for your sales team.

**Bonus Read:&#x20;**[**Top 5 Python Libraries That Speed Up AI Development**](https://advantailabs.com/blog/top-5-python-libraries-that-speed-up-ai-development)

### **(3) Smart Content and SEO Agents**

- **What it is:&#x20;**&#x41;I agents that help with your marketing by finding popular topics, writing blog posts and social media updates, and making sure more people see your content online.
- **What it uses:&#x20;**&#x41;I to write like a person, machine learning to find the best topics and keywords, and data analysis to see what content works best.
- **Benefits:&#x20;**&#x59;ou can create more content, get more visitors to your website, and save money on writing and SEO services.
- **The 2025 Edge:&#x20;**&#x54;hese agents will create content that changes in real-time based on who is looking at it, making it a unique experience for every person.

### **(4) Smart Supply Chain and Inventory Agents**

- **What it is:&#x20;**&#x41;I agents that look over your supply chain, try to predict how much of a product you'll need, manage your stock, and help you avoid problems with suppliers.
- **What it uses:&#x20;**&#x53;mart AI that predicts demand, real-time data from sales your business makes and other sources, and decision-making for ordering and shipping automatically.
- **Benefits:&#xA0;**&#x53;ometimes, when you run out of a product or have too much, lower shipping costs, and a supply chain that can handle surprises.
- **The 2025 Edge:&#x20;**&#x54;hese agents will have conversations with your systems to adjust plans, like changing prices based on how much stock you have.

### **(5) Smart HR and Employee Agents**

- **What it is:&#x20;**&#x41;I agents that make HR work easier for your company, from hiring new people and getting them ready, to answering employee questions and helping with paperwork.
- **What it uses:&#x20;**&#x41;I to understand what employees and candidates are asking for, Managing HR tasks automatically, and data analysis to find the best people for the job.
- **Benefits:&#x20;**&#x46;aster hiring, a better experience for new employees, and more time for your HR team to focus on important things like employee growth and happiness.
- **The 2025 Edge:&#x20;**&#x54;hese agents will help with an employee's whole journey, from creating personal development plans to checking in on how they are feeling about their job.

### **(6) Smart Cybersecurity Agents**

- **What it is:&#x20;**&#x41;Is that are always watching your computer systems for threats. If they find something, they can act right away to stop it before it causes damage.
- **What it uses:&#x20;**&#x53;mart machine learning to get better at finding new threats, predictive AI to guess where attacks might happen, and automated tools to fix problems quickly.
- **Benefits:&#x20;**&#x46;aster response to cyberattacks, less risk of losing data or having your systems go down, and a stronger defense that is always learning.
- **The 2025 Edge:&#x20;**&#x54;his will be like having a smart AI security team that knows what's happening across your whole business and can stop threats before they start.

### **(7) Personalized Employee Training Agents**

- **What it is:&#x20;**&#x41;gents that create personal training plans for each employee. They see what skills an employee has, what they want to learn, and give them the right training at the right time.
- **What it uses:&#x20;**&#x53;mart data analysis to understand an employee's skills, adaptive learning to change the training as they go, and AI to act as a coach.
- **Benefits:&#x20;**&#x4D;ore interesting and effective training, a smarter and more skilled team, and happier employees who feel like the company is investing in them.
- **The 2025 Edge:&#x20;**&#x54;hese agents will be like personal career coaches, helping employees grow, learn new skills, and reach their goals.

### **(8) Automated Financial Agents**

- **What it is:&#x20;**&#x41;I agents that make finance work easier by collecting financial data, creating reports, finding mistakes or fraud, and helping with rules and regulations.
- **What it uses:&#x20;**&#x41;dvanced tools to collect and check data, machine learning to find patterns and problems, and AI to understand financial documents.
- **Benefits:&#x20;**&#x4D;ore accurate financial reports, faster decision-making, less risk of fraud, and more time for your finance team to focus on strategy.
- **The 2025 Edge:&#x20;**&#x54;hese agents won't just find problems, they will look into them, suggest how to fix them, and in some cases, fix them on their own.

### **(9) Smart Meeting and Workflow Agents**

- **What it is:&#x20;**&#x41;I agents that make meetings and daily work easier. They can schedule meetings, create agendas, take notes, and make sure everyone knows what they need to do afterward.
- **What it uses:&#x20;**&#x41;I to understand what is said in meetings, automation to handle scheduling and tasks, and connections to your calendar and other tools.
- **Benefits:&#x20;**&#x4C;ess time spent on boring meeting tasks, more productive meetings, and smoother projects because everyone is on the same page.
- **The 2025 Edge:&#x20;**&#x54;hese agents will be able to understand complex documents and help with difficult decisions, and they will be able to look at many meetings to find common themes or problems.

### **(10) AI Market Research Agents**

- **What it is:** AI agents that are always looking at what's happening in your market. They watch social media, news, and what your competitors are doing to find new trends and opportunities.
- **What it uses:** Smart tools to collect information from all over the internet, AI to find patterns, and predictive AI to guess what might happen next.
- **Benefits:** Faster and better information about your market, lower research costs, and the ability to make quick decisions based on what's happening right now.
- **The 2025 Edge:** These agents will not just tell you what's happening; they will help you predict what will happen next and give you advice that is tailored to your business.

If you also want to automate your business with AI Agents, you can check out our[ Agentic AI Workflow Building services](https://advantailabs.com/services/agentic-ai-workflow-building) and get a free consultation.

## Conclusion: The Future is Here Is Your Business Ready?

AI Agents are changing the way businesses work. They can make your customer service better, your sales faster, and lift your business to new heights. Their ability to learn and work on their own is a big step up from older technologies. The big question for business leaders is not _if_ you will use AI agents, but _how_ and _when_. The opportunity is huge for those who are ready to explore and use these powerful tools. The future of business is here, and it's time to get ready.

Top 5 Python Libraries That Speed Up AI Development

Python is a crucial language in AI and Machine Learning due to its extensive libraries and frameworks, making it easy to work with. These features make Python the most suitable and preferred language for building and deploying machine learning (ML) models, as well as simplifying complex tasks.

As you have just read above, “Python makes tasks and complexity easy by its extensive libraries and frameworks. In this article, we are going to explore the top 5 Python libraries that will help you build your AI project rapidly. The libraries include features like pre-built algorithms & models, “ for example, you can train a [logistic regression](https://www.geeksforgeeks.org/understanding-logistic-regression/) or a [CNN](https://en.wikipedia.org/wiki/Convolutional_neural_network) with just a few lines of code.”, optimized performance, simplified workflows, access to pretrained models, and many more.

## **Top 5 Python Libraries for Fast AI Development**

### **5 Python Libraries + What They Do**

#### **1. TensorFlow:**

[TensorFlow](https://www.tensorflow.org/) is an end-to-end machine learning platform by Google. It provides features like a complete ecosystem to build, train and deploy machine learning and deep learning models at scale. TensorFlow supports many things like from simple neural networks to complex neural networks. For tasks like image recognition, natural language processing, and time series forecasting.

##### Why it’s helpful:

TensorFlow is full of features that can speed up AI Development.

- **High-level & low-level APIs:&#x20;**&#x54;ensorFlow supports both the beginner using Keras for easy model building or an expert requiring fine-grained control.
- **GPU & TPU Acceleration:&#x20;**&#x69;f you are training models on CPU can take hours or days on the other hand, TensorFlow supports hardware acceleration like GPU & TPU, which will speed up computation time drastically.
- **Pre-Trained models:&#x20;**&#x49;n TensorFlow Hub, you can use the library of reusable model components. Developers can use these Pre-Trained models to fine-tune solutions without starting from scratch.
- **Deployment-Ready:&#x20;**&#x54;ensorFlow has 3 native inbuilt tools like **TensorFlow Serving&#x20;**&#x66;or model deployment, **TensorFlow Lite** for mobile and embedded devices, an&#x64;**&#x20;TensorFlow JS&#x20;**&#x66;or running models in the browser.

##### Real world case:

- Google Translate and photos use TensorFlow for NLP and image recognition.

> TensorFlow is a robust, flexible, and scalable library that supports the full lifecycle of machine learning—from research and experimentation to production and deployment.

#### **2. PyTorch:&#xA0;**

[PyTorch](https://pytorch.org/) is a very popular open-source deep learning framework developed by Meta (Facebook). PyTorch is mainly used for building and training neural networks, and now it is a go-to library for both academic research and industrial applications.

PyTorch provides Tensor computation (similar to [numpy](https://numpy.org/)) with strong GPU support and a very deep integration of automatic differentiation, which is necessary for training deep learning models.

##### Why it’s helpful:

- **Dynamic Computation Graphs (Define-by-Run)**: PyTorch builds the computational graph on the fly during execution. This makes it **predictive, easy to debug, and flexible**, especially for complex or changing model architectures. Different than static graph libraries like early TensorFlow versions.
- GPU Acceleration: PyTorch lets you use the GPU(s) for faster computation. A single **.to('cuda')** command can move your model to the GPU for significantly faster training.
- **Ideal for Research and Prototyping**: Because of its nature and dynamic graphs are like Python, PyTorch is **preferred by researchers** for its flexibility to use it effortlessly when experimenting with new ideas.
- **Extensive Ecosystem**: PyTorch’s ecosystem includes:

1. **TorchVision** for computer vision tasks
2. **TorchText** for NLP
3. **TorchAudio** for audio applications
4. **PyTorch Lightning** for clean, scalable training code
5. **Hugging Face Transformers** (fully compatible with PyTorch)

- **Production-Ready**: You can optimize PyTorch models for deployment in production environments across platforms with TorchScript and integration with ONNX.
- **Strong Community & Documentation**: PyTorch has an active open-source community and excellent documentation, tutorials, and tools that can make your learning and implementing models easier.

##### Real world case:

Used by organizations like **Tesla, Microsoft, and OpenAI** for various AI applications.

> PyTorch is a flexible, user-friendly framework that is used in research and prototyping, yet is powerful and mature enough for large-scale production AI systems.

#### **3. Scikit-learn**

[**Scikit-learn**](https://scikit-learn.org/stable/)**&#x20;**&#x69;s the best library and is used by worldwide professionals for classical machine learning in Python, it is built on NumPy, [SciPy](https://scipy.org/), and [matplotlib](https://matplotlib.org/), it provides the best tools for tasks like **classification, regression, clustering, dimensionality reduction, and model evaluation**.

It’s specifically known for its **clean API design**, making it easy to use, learn, and integrate into machine learning pipelines.

##### Why it’s helpful:

- **Quick Prototyping**: If you want to test our algorithms like Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, and K-Nearest Neighbors with just a few lines of code, you can use Scikit-learn, and it will make it easy for you.
- **Comprehensive Utilities**: Beyond just algorithms, it includes:

1. Preprocessing tools (e.g., normalization, encoding)
2. Feature selection
3. Model evaluation metrics
4. Hyperparameter tuning tools (e.g., GridSearchCV, RandomizedSearchCV)

- **Consistent API Design**: In Scikit-learn, every single model follows a consistent syntax (.fit(), .predict(), .score()), so if you learn just one model, you can use all of them; this will speed up learning and experimentation.
- **Perfect for Small-to-Medium Datasets**: It is perfect and well-suited for structured/tabular data problems, especially when you are working with datasets that fit in memory
- **Seamless Integration**: Scikit-learn works amazingly with **pandas** for data handling and **matplotlib** for data visualization. You can make complete ML workflows within a single notebook.
- **Useful for Baseline Models**: Scikit-learn is mostly used for establishing baseline performance before moving to more complex models like deep learning.

##### Real world case:

- Used in finance for fraud detection and credit scoring
- Applied in marketing for customer segmentation and churn prediction
- Common in healthcare for diagnostic prediction models

> Scikit-learn is the go-to toolkit for fast, clean, and effective implementation of classic machine learning algorithms perfect for anyone working with structured data or starting out in ML.

#### **4. Hugging Face Transformers**

[**Hugging Face Transformers&#x20;**](https://huggingface.co/docs/transformers/en/index)is an open-source library that provides you with thousands of pre-trained models for Natural Language processing(NLP) and Natural Language Understanding tasks, it gives you features like modern transformer architectures like **BERT**, **GPT**, **T5**, **RoBERTa**, **DistilBERT**, and many more.

These models can be used for a wide range of language-related tasks such as **text classification, sentiment analysis, question answering, summarization, translation, and conversational AI**.

##### Why it’s helpful:

- **Pre-trained State-of-the-Art Models**: if you are training large models from scratch, stop it right there. Hugging Face Transformers gives you pretrained models that you can fine-tune with your dataset with less code and compute.
- **Rapid Development:&#x20;**&#x41;nother Benefit is that with Hugging Face, you can load a model and start performing complex NLP tasks with just a few lines of code. This is incredibly useful for prototypes, hackathons, or production-ready solutions.
- **Multi-Framework Support:** It gives flexibility to developers and works seamlessly with both PyTorch and TensorFlow, based on their preferred ecosystem.
- **Tokenizers & Pipelines**: This library contains optimized tokenization tools and “pipeline” abstractions that can do common tasks (like sentiment analysis) so you can get results with a single function call.
- **Great for&#x20;**[**Chatbots & AI Assistants development**](https://advantailabs.com/services/generative-ai-product-engineering/ai-chatbot-development): Hugging Face is the best option for building conversational interfaces using models like **GPT-2/3**, **DialoGPT**, or **BlenderBot**.

##### Real world case:

- Customer support chatbots
- Automated content moderation
- Text summarization and document analysis
- Sentiment analysis for social media monitoring

> Hugging Face Transformers revolutionizes [NLP development ](https://advantailabs.com/services/ai-ml-engineering/natural-language-processing)by offering plug-and-play access to powerful language models with less development time from weeks to minutes.

#### **5. ONNX (Open Neural Network Exchange)**

[**ONNX** ](https://onnx.ai/)is an open-source platform that is made for representing machine learning models in a framework. It helps developers to train a model in just one framework (such as PyTorch) and then export it to run in different environments (such as TensorFlow, [Caffe2](https://caffe2.ai/), or a production inference engine).

ONNX was first developed by Microsoft and Facebook and is now supported across the AI ecosystem.

##### Why it’s helpful:

- **Cross-Framework Compatibility**: ONNX lets you move models effortlessly between different deep learning frameworks. For example, you can train a model using PyTorch and deploy it using TensorFlow Lite or OpenVINO, without rewriting your codebase.   
- **Optimized Inference**: ONNX models can be run using the **ONNX Runtime**, a tremendous inference engine that supports hardware acceleration (CPU, GPU, and specialized chips like NVIDIA TensorRT or Intel’s OpenVINO). This will make model serving faster and more efficient
- **Production Deployment**: There are many platforms that prefer the ONNX Format for easier integration, especially those focused on mobile, embedded systems, and cloud environments. This makes it a really good choice for **edge AI** and **enterprise deployment.**
- **Wide Ecosystem Support**: ONNX supports many ML frameworks and tools like PyTorch, TensorFlow, Keras, MXNet, scikit-learn, and more. It’s also integrated into cloud platforms like Azure ML and AWS SageMaker.
- **Model Portability & Longevity**: ONNX takes care of your model remains usable and portable even if the original training framework is no longer available or updated. It behaves like a universal model language

##### Real world case:

- Deploying AI models trained in Python to production environments built in C++ or Java
- Running a PyTorch-trained model on a mobile app using TensorFlow Lite
- Integrating models into IoT devices or smart cameras

> ONNX removes the problem of AI deployment by making models framework-independent, enabling developers to build once and deploy anywhere with optimized performance.

Google I/O 2025 : Google is skyrocketing in AI

On May 20th, at the Shoreline Amphitheater in Mountain View, California, Google I/O 2025 was held, and they showcased major AI breakthroughs, one of the hot topics being Google AI Mode. For those wondering what Google AI Mode is, it is an end-to-end AI Search experience coming to everyone in the US starting on May 20th. In this article, we are going to cover the key features, highlights, summary, and examples to familiarize you with Google AI Mode so that you can use it more effectively in your day-to-day life. Whether you want to use it to speed up your browsing, shopping, and getting real-time personalized suggestions, or you want to use it to become more productive at your workspace, this article will help you with everything you need to know to get started.

## **How will AI Mode help you with your browsing?**

So, let’s familiarize you with the features of [Google AI Mode](https://search.google/ways-to-search/ai-mode/). With AI Mode, you can now ask anything, and a more intelligent, agent-like, personalized search will give you answers to your toughest questions. You will find it as a new tab directly in Search or right from your search bar.

With AI Mode, you can ask whatever is on your mind, and you will see AI Mode start its work to provide you with a generated response, putting everything together for you, including links to content and creators you might not have discovered, and merchants and businesses with useful information like ratings.

Search uses AI to dynamically adapt the entire UI, which will make your experience 100 times better. This will include images, combinations of text, and links, even the map, just for your question. You can also follow up with your questions or doubts, just like ChatGPT. Soon, AI Mode will be able to make your responses even more helpful with personalized suggestions based on your past searches, your profile data, and recently visited sites.

Not just that, AI Mode also has deep research capabilities configured in it. Deep Search uses the query fan-out technique that you might have heard about, but multiplied; it can issue dozens or hundreds of searches on your behalf and create a fully cited report in just minutes. It includes links to the web throughout, so you can easily explore and take action.

## **Complex analysis & data visualization**

You might sometimes just get bored of browsing the internet or performing searches in search engines to find or get insight. The data you’ll have to go through so many web pages to get the correct information, and it is a pretty slow and boring process, but with Google AI Mode, not anymore. It has a feature integrated that can do complex analysis and make graphs and data visualizations, which can help you in many cases.

For example, you are a huge baseball fan, and you want to know who made a huge impact in the last matches. You want to know the numbers, so you just ask, “Show the batting average and base performance for this season.” You will get a helpful response, including a table search, and you can also ask follow-up questions like, “How many home runs have these players hit this season?” and you’ll get a graph with a dynamically generated UI.

## **Search Live in AI Mode**

Now, let’s talk about what multimodality means. [Search Live](https://blog.google/products/search/google-search-ai-mode-update/#live-capabilities), using your camera, the search can see what you see and get helpful information as you go back and forth in real time.

## **AI Mode helps you with your shopping**

With AI Mode, Google is bringing a new level of intelligence to help you [shop with Google](https://blog.google/products/search/google-search-ai-mode-update/#shopping).

For example, you are looking for a rug to lighten the room, but you are afraid that it might not look good with surrounding objects, so you search, “I have a light gray couch, and I am looking for a rug that will lighten the room.” AI Mode will dynamically generate a browsable mosaic image and some shoppable products personalized just for you. This smart AI not only gives you the product, it will also take a look at the other things based on your prompts and think about what kind of rug and what color of rug will help you with a light gray couch.

## **Try On feature in AI Mode**

Now, this feature will change your online shopping. With this Google AI Mode feature, you can shop without worrying about how a specific dress or clothes will look on you. With Google AI Mode’s Try On feature, you can virtually try on the outfit you see as a product.

For example, you want a new dress for summer. A normal search provided you with normal options, but now you are facing a huge dilemma about which dress you should shop for and which will look good on you. So, just to solve this problem, you can use the Google Try On feature, in which you can try on the products virtually so you get a feel for how styles might look on you. It will ask you to upload a picture of yourself; this will take you to your camera roll. Select a picture with a full length and a clear view of you. Then, you can try on whatever you want to try on and shop happily.

There are many announcements that were made by google, and advant will bring more news like this in the future. if you like this post read other articles on our website, and also if you are looking for any AI development related services we also got your back we provide services like [AI ChatBot Development services](https://advantailabs.com/services/generative-ai-product-engineering/ai-chatbot-development) to [blockchain engineering](https://advantailabs.com/services/blockchain-engineering).

Meta's LlamaCon Announcements: A New Chapter in AI

On April 29, 2025, Meta held its first LlamaCon at its headquarters in Menlo Park, California, a big moment in the company’s journey with AI. The event was livestreamed around the world on the Meta for Developers Facebook page and YouTube channel, giving people a look into how Meta is working on making AI more accessible through open-source tools. One of the biggest announcements was a new standalone AI chatbot app, meant to compete with apps like ChatGPT. In this post, we’ll break down what Meta shared, why it matters, and what it could mean moving forward.

## **LlamaCon: Focusing on What’s Next for AI**

LlamaCon brought together developers and leaders from the tech world to talk about where generative AI is headed. Talks from Meta’s Chief Product Officer Chris Cox, VP of AI Manohar Paluri, and researcher Angela Fan highlighted how widely the company’s open-source Llama models are being used. These models have been downloaded hundreds of millions of times and are being used by companies like Goldman Sachs, AT\&T, DoorDash, and tech partners like Nvidia and Snowflake, making them a key part of today’s AI tools.

The event also included relaxed conversations with Databricks CEO Ali Ghodsi and Microsoft CEO Satya Nadella, where they shared their thoughts on how open-source work is influencing the future of AI. These chats reflected Meta’s view of how AI tech is evolving and how important collaboration will be along the way.

## **A New Chatbot App Focused on Real Conversations**

Meta also announced a new standalone chatbot app and it’s trying something different. Instead of just answering questions, the app comes with a social feed where users can share AI-generated chats. It also uses information from Facebook and Instagram to offer replies that feel more tailored to each person. This approach could make AI chats feel more natural and personal not just like using a tool, but like having a digital companion that gets you.

### **Why That’s Interesting**

This is Meta’s way of going head-to-head with ChatGPT and similar tools. By mixing social sharing with personalization, the company is aiming to make AI feel less robotic and more like something you'd want to talk to regularly. That could change how people interact with AI from simple Q\&A to more meaningful, ongoing conversations.

## **Giving Developers Tools That Actually Help**

Meta didn’t just focus on users, they also announced a new cloud API for developers to access Llama models. This means developers can now more easily add Meta’s AI into their apps. It’s a clear move to make AI development more open and flexible, especially compared to companies like OpenAI that keep things more locked down.

### **Why Developers Might Care**

This API gives developers a straightforward way to build their AI-powered features without jumping through too many hoops. It fits with Meta’s bigger plan to grow the use of its models by letting more people build with them. If you’re thinking about creating your chatbot, a company like Advant can help bring that idea to life with [AI Chatbot Development services](https://advantailabs.com/services/generative-ai-product-engineering/ai-chatbot-development).

## **Looking Ahead: Meta’s Bigger Plan for AI**

All these LlamaCon updates are part of something larger, Meta’s long-term play in the AI space. The company plans to invest as much as $80 billion into AI this year, covering new hires, advanced data centers, and deeper research. That kind of money shows how seriously Meta is taking the competition, especially against companies like OpenAI.

### **The Ripple Effect**

By putting money and effort into open-source AI and making tools more available, Meta could speed up how fast new ideas come to life. That could lead to real change across industries from healthcare to finance, making things possible that used to be out of reach.

## **Wrapping Up: A Big Push From Meta**

LlamaCon wasn’t just a tech conference it marked a real turning point for how Meta is approaching AI. With the launch of a social-friendly chatbot app and a developer-ready API, the company is trying to make AI more open and useful for everyone. If Meta continues down this path, it could not only stay competitive but also help build a more open and creative AI world where anyone with an idea can make something happen.

Exploring the OpenAI Codex CLI: A Developer’s Guide to AI-Powered Coding

The OpenAI Codex CLI tool lets you harness AI to assist with coding directly from your computer’s command line. It can generate code, fix tasks, or even build entire applications. This guide will walk you through setting it up and trying it out with a simple to-do app example, designed to be straightforward for anyone to follow!

## **Why Use This Tool?**

Here’s what makes it stand out:

- **Saves Time**: Speeds up coding by assisting.
- **Integrates easily**: Works seamlessly with your existing tools.
- **Versatile**: Handles both small fixes and large projects.

Let’s dive in!

## **Before You Start: Get Node.js**

You’ll need[ Node.js](https://nodejs.org/en/download/) installed first. It’s a runtime that lets you run JavaScript on your computer. Here’s how to set it up:

1. **Download It**: Visit the[ Node.js website](https://nodejs.org/en/download/) and select the version for your operating system (Windows, macOS, or Linux).
2. **Install It**: Run the downloaded file and follow the installation instructions.
3. **Verify It**: Open your command line—Terminal on macOS/Linux or Command Prompt on Windows—and type:

```
node --version
```

You should see a version number like v18.17.0. If not, reinstall and try again.

**Note**: Node.js is essential for the Codex CLI, so don’t skip this step!

## **Step 1: Install the Tool**

Once Node.js is ready, install the Codex CLI by entering this command in your command line:

```
npm install -g @openai/codex
```

This installs the tool globally, so you can use it from anywhere on your computer.

**Troubleshooting**:

- Ensure you have permission to install (on macOS/Linux, try adding sudo before the command).
- Confirm your internet connection is active.
- New to npm? Check out the[ npm documentation](https://docs.npmjs.com/) for more details.

## **Step 2: Add Your OpenAI Key**

The tool requires an API key to connect to OpenAI’s services. Here’s how to set it up:

**(1). Sign In**: Go to[ platform.openai.com](https://platform.openai.com/) and log in or create an account.

**(2). Generate a Key**: Navigate to the “API Keys” section, click “Create New Secret Key,” name it (e.g., “Codex Key”), enable all permissions, and click “Create Secret.”

**(3). Store It Safely**: Copy the key to a secure location—it’s only shown once.

**(4). Set It Up**: Add the key to your system with the appropriate command:

**macOS/Linux**:

```
export OPENAI_API_KEY='your-key-here'
```

**Windows (Command Prompt)**:

```
set OPENAI_API_KEY='your-key-here'
```

**Windows (PowerShell)**:

```
$env:OPENAI_API_KEY='your-key-here'
```

 

Replace 'your-key-here' with your actual key.

**(5). Keep It Private**: Never share your key. You can save it in your environment variables to avoid retyping it.

**(6). Verify Your Account**: Ensure it’s active under the “General” settings on the OpenAI platform.

## **Step 3: Make a Project Space (Optional)**

It’s a good idea to create a dedicated folder and use[ Git](https://git-scm.com/doc) to track your changes:

**Create a Folder**:

```
mkdir codex-app
cd codex-app
```

**Initialize Git**:

```
git init
```

This sets up a workspace and lets you manage your project’s history.

**Why Git?**: It helps you revert mistakes easily. New to Git? Explore the[ Git documentation](https://git-scm.com/doc).

## **Step 4: Use the Tool**

Launch the tool by typing:

```
codex
```

If Git isn’t configured, you might see a message, but you can proceed anyway.

For more information on the Codex CLI, refer to the[ OpenAI API documentation](https://platform.openai.com/docs/).

### **How It Works**

- **AI Model**: It uses 04-mini to perform tasks.
- **Safety Feature**: Runs in suggest mode by default, asking for approval before making changes.
- To enable full automation, type:

```
approval full-auto
```

**Tip**: Start with suggest mode to understand its behavior.

### **Try It: Make a To-Do App**

Let’s create a simple to-do app:

**(1). Give the Command**:

Create a React web app with Shad CN that is a to-do app with persistence

**(2). Wait**: The tool will generate the app files.

**(3). Run It**: Follow the provided instructions, typically:

```
npm run dev
```

This launches the app in your browser, where you can add tasks.

**Result**: A functional app you can customize. Want to learn more? Check out[ React](https://reactjs.org/) and[ ](https://shadcn.dev/)[Shad CN ](https://ui.shadcn.com/docs)documentation.

## **Step 5: Watch the Cost**

Using the Codex CLI incurs costs based on usage. Here’s how to monitor it:

- **Check Usage**: Visit[ platform.openai.com](https://platform.openai.com/) and go to “Usage.”
- **Cost Estimate**: A to-do app might cost around $0.71, but this varies.
- **Pro Tip**: Monitor regularly to avoid surprises.
- Curious about pricing? See the[ OpenAI pricing page](https://openai.com/pricing)

## **If Something Goes Wrong**

Common issues and fixes:

- **Installation Fails**: Verify permissions and internet connectivity.
- **Key Issues**: Double-check the key and account status.
- **Tool Not Found**: Reinstall or review your setup.
- **Odd Output**: Rephrase your command.
- Need more help? Visit[ OpenAI’s help pages](https://help.openai.com/).

## **Easy Tips**

- **Review Output**: Always check the generated code.
- **Leverage Git**: Protect your work with version control.
- **Start Small**: Test with simple tasks first.
- **Secure Your Key**: Keep it confidential.

## **Wrap-Up**

The OpenAI Codex CLI is a powerful way to integrate AI into your coding workflow. This guide walked you through installation and building a basic app. Now, experiment with it on your projects to streamline your coding!

Need assistance with an AI project? Explore our[ AI/ML Research & Development services](https://advantailabs.com/services/ai-ml-research-development) to see how we tackle real-world challenges with AI.