What are AI Agents? The Ultimate Guide

AI agents are capable of many things, such as perceiving and interacting with their environment, creating step-by-step plans to reach a goal, and taking autonomous action.

Think of them as acting like high-performing employees, providing businesses with reliable and capable hands-on workers who know how to get the job done right. Businesses that tap into these powerful capabilities can expect to reap numerous benefits.

Boosted efficiency and productivity

AI agents perform tasks autonomously — without human intervention. That means employees can delegate certain tasks to AI systems while spending their time on other, more strategic initiatives. With humans and AI agents furthering organizational goals, a business can get more done in less time.

“AI agents significantly boost productivity by automating complex tasks, providing unparalleled customer service with 24/7 availability, and offering powerful data analysis capabilities,” says Paul Ferguson. “I’ve seen teams halve their workload and businesses gain unexpected insights from properly implemented AI agents.”

…In the future, everyone is going to become a manager because, as an individual contributor, you have very limited time and you don’t have much leverage. But what if you could have all these AI agents working for you, doing the job for you, right?

What would happen is that instead of doing something yourself, you just hand over the task to an agent that accomplishes the task for you, and then you actually do something else. And basically, you start thinking like a manager, as opposed to thinking like an individual contributor. You are becoming this person who’s actually kind of telling what to do to these AI agents so that you can actually manage them.

Aytekin Tank, Founder & CEO, Jotform

Enhanced decision-making

“AI agents analyze large datasets to offer insights and recommendations, helping businesses make more informed decisions,” says Ahmed Elmahdy. These systems not only complete tedious and repetitive tasks associated with data collection and analysis but can help employees make complex and strategic decisions based on the data provided to them.

AI agents are capable of identifying trends and patterns in data that might be difficult for humans to discern. They can also process data in near real time, create data visualizations to aid in understanding, and generate detailed reports.

For employees and managers who rely on data for decision-making, AI agents can provide a superior level of data analysis quickly and accurately.

Round-the-clock availability and scalability

Most businesses don’t run 24-7 — but today’s customers expect around-the-clock availability. They want to be able to ask a question in the middle of the night or get a response on a complex issue first thing in the morning.

“AI agents can work 24-7 and handle large volumes of tasks, making them ideal for scaling operations without significantly increasing costs,” says Elmahdy.

Businesses that implement AI agents can more easily meet the increasing demands of customers while keeping their costs in check.

Tailored customer experiences

At the heart of every great customer experience is personalization. Customers aren’t satisfied with generic marketing and customer service. They want businesses to consider their needs and preferences. If a business fails to do so, the customer may go to a competitor.

AI agents support businesses in tailoring customer experiences by analyzing customer data within seconds, providing customized product recommendations, engaging with customers in a personalized way, and offering quick and detailed responses to queries. These actions contribute to increased customer conversions and loyalty.

Cost savings and revenue growth

Businesses continuously strive to improve their bottom line. AI agents help organizations better manage costs by improving process effectiveness, automating manual tasks, and minimizing human errors and work repetition. AI agents can even adapt to changing software environments, evolving alongside the business.

Making your operations speedier and more efficient also improves customer satisfaction. Customers receive their products and services more quickly and with better results, encouraging them to continue engaging with the business. This strategy drives revenue over the long term and boosts your bottom line.

Innovations and opportunities

“One of the key benefits of AI agents is their efficiency; they automate repetitive tasks, which allows organizations to streamline operations and boost productivity,” says Jason Sherman, CEO and cofounder of Vengo AI, an artificial intelligence tool for transforming sales conversations. 

Efficiency leads to innovation. Thanks to AI agents, your employees can spend less time on routine, repetitive activities and more time on high-level tasks that will better benefit the business.

For some employees, this means they’ll have the opportunity to be creative and explore innovations or solve challenging customer issues. Others can spend more time thinking about the bigger picture of the business and making strategic decisions for the future.

By placing more emphasis on these activities, you could see your business heading in exciting new directions.

Now that you know the key benefits of AI agents, it’s time to understand how this technology works. Head to the next section to learn about the basics of AI agent operations and the different types of AI agents available.

Understanding how AI agents work can help determine whether one is right for your business needs.

“AI agents rely on machine learning algorithms to process inputs, make decisions, and learn from the outcomes,” says Ahmed Elmahdy. “They interact with users or systems, gathering data and refining their decision-making processes over time.”

These tools can not only evolve with your business but help your business evolve as well. AI agents can help you achieve certain goals faster and more effectively. In this chapter, you’ll learn about how agentic AI works and the different types of AI agents that exist.

Operational mechanics of AI agents

Let’s start with a big-picture view. What are the foundational characteristics of an AI agent?

• Input-output and data-processing components: AI agents have sensors and processing mechanisms to perceive their environment, gather and analyze data, and make decisions. They also have effectors to generate and deliver output — responses based on decisions they’ve made.
• Machine learning: Machine learning is an aspect of artificial intelligence that enables AI to learn and imitate human decision-making using data and algorithms. AI agents use machine learning to improve the accuracy of their predictions and decisions over time.
• Natural language processing (NLP): NLP is a branch of AI that enables it to understand human language. “AI agents gather data, analyze it to identify patterns, and take actions based on their findings, often leveraging natural language processing to interact with users,” says Jason Sherman. NLP enables AI to “talk” to humans, understand instructions, offer replies, and ask questions.
• Integration: Artificial intelligence technology must have access to other systems to get necessary data. It uses APIs to integrate with other systems. 
• Improvement: Unlike other types of technology, artificial intelligence improves by design. AI agents continuously learn from data and feedback, enhancing their performance to achieve better results.
• Guardrails: All technology, especially artificial intelligence, requires boundaries. This is where large language model (LLM) guardrails come in. “To ensure safety and ethical use, we implement LLM guardrails that provide guidelines to prevent harmful outputs and maintain user privacy,” says Sherman. There are different types of LLM guardrails, such as ethical, compliance, and security guardrails, which govern the behavior and output of the AI. 

How do AI agents work?

So, how do all those components work together to make AI agents capable of understanding human instruction, making decisions, and completing tasks?

“At their core, AI agents operate on a continuous cycle of perception, reasoning, and action,” says Paul Ferguson. “They ingest data from their environment, process it using preprogrammed rules and learned patterns, and then execute actions based on their analysis. This process allows them to adapt to various situations and improve over time.”

Not all AI agents work the same way (more on that later), but in general, they follow a similar pattern of operation:

1. Establish a goal. AI agents are programmed to achieve certain objectives. The first step is for a user (a human or possibly another software system) to define the goal, which could be anything from sending an email to driving a route. 
2. Break down the steps. Once the AI agent has a goal, it determines the necessary steps to achieve it and the order in which to take those steps. 
3. Gather data. Next, the AI agent gathers the information required to perform the task. It is capable of gathering relevant data from a wide variety of sources, such as the internet, other AI agents, databases, calendars, conversation logs, and more. Learning this new information not only helps the AI agent achieve its current goal but also provides valuable training data it can apply to completing future goals.
4. Complete tasks. With data in hand, the AI agent begins fulfilling the necessary tasks. It may need to gather further data if it runs up against an obstacle.
5. Reflect on learnings. Once the goal is achieved, the AI agent uses multiple feedback mechanisms to reflect on its performance and the information learned along the way. This data is then used to enhance the AI agent’s capabilities for the next goal.

As noted previously, LLM guardrails are the boundaries placed on AI agents to manage their behavior and ensure the right outcomes when completing goals.

“LLM guardrails are critical safety measures for AI systems, especially those using large language models,” says Ferguson. “They ensure AI agents operate within ethical, legal, and operational boundaries. I always emphasize their importance to clients, as they prevent potential PR disasters and maintain user trust.”

Types of AI agents

While most AI agents operate in a similar way, it’s important to note that not all AI agents are the same, and they’re not designed to achieve the same types of goals.

“There’s no one-size-fits-all in AI agents,” says Ferguson. “They range from simple reflex agents for straightforward tasks to complex learning agents that improve with experience. The key is choosing the right type for your specific needs.”

Social AI agents

Social AI agents are designed to interact with people and other AI agents in a socially intelligent manner. They leverage natural language processing, machine learning, and behavioral modeling to understand, predict, and respond to social cues such as language, tone, and context.

These agents are used in applications like customer service chatbots, virtual assistants, and collaborative robots (cobots) to facilitate human-like interactions, enhance user experiences, and support teamwork in both virtual and physical environments. Their goal is to build trust, empathy, and effective communication in human-AI interactions.

Automation AI agents

Automation AI agents are built to perform tasks, processes, and workflows with minimal or no human intervention. They use machine learning, predictive analytics, and rule-based logic to streamline repetitive, time-consuming activities across various industries.

These agents can handle data processing, decision-making, and task execution in areas like customer support, supply chain management, and software testing. By reducing manual effort and increasing efficiency, automation AI agents help organizations improve productivity, reduce errors, and lower operational costs.

Collaborative AI agents

Collaborative AI agents are designed to work alongside people or other AI agents to achieve shared goals. They use machine learning, natural language processing, and adaptive reasoning to facilitate teamwork, communication, and decision-making.

Unlike standalone automation, these agents actively coordinate with human users, offering suggestions, sharing insights, and responding to feedback in real time. Collaborative AI agents are commonly used in areas like healthcare, education, and project management, where human-AI cooperation enhances problem-solving, creativity, and overall productivity.

Other most common AI agent types

Simple reflex agent

A simple reflex agent is designed to complete straightforward tasks autonomously. This AI agent doesn’t make plans, seek additional data, or interact with other software — it can only complete the action(s) it’s programmed to do. If it runs into an obstacle or can’t complete its actions, it doesn’t know how to respond.

Example: An automatic light dimmer switch programmed to turn off the lights at 11 p.m.

Model-based reflex agent

A model-based agent has more advanced decision-making skills than a simple reflex agent. It also has memory and learns from past experiences in its environment, building an internal model of the world it perceives.

Example: A self-driving car

Goal-based agent

A goal-based agent has complex reasoning and decision-making capabilities. It builds an internal model of its world and is programmed to take the most efficient route to achieve its goal based on the data it collects.

Example: A GPS navigation tool

Utility-based agent

A utility-based agent is designed to achieve a goal while maximizing the reward for the user, based on a metric for utility. For example, for an AI agent that’s programmed to find the cheapest flight to a destination, the “metric for utility” is the cost of the flight. The lower the cost, the higher the reward for completing the action.

Example: A delivery drone

Learning agents

A learning agent is programmed to learn from each of its experiences in order to improve performance for the next experience. It uses data, sensory inputs, feedback mechanisms, and other information to better perform its tasks.

Example: A spam-filter learning agent

Hierarchical agents

Hierarchical agents consist of multiple agents programmed to work together. The higher-level agents are responsible for complex tasks and assigning the lower-level agents subtasks or simple tasks to complete.

Example: A customer service hierarchical agent that assigns lower-level agents to send simple responses to customers while a higher-level agent completes returns and shipping processes

Now that you have a foundational understanding of how AI agents work and the different types of AI agents, it’s time to put all this knowledge to good use. In the next two sections, we’ll explore how to implement an AI agent in your workplace and the various ways in which they’re currently being used.

If you’re considering implementing a custom AI agent to improve productivity, save on costs, or enhance results for customers, be sure not to rush into anything. If you’re building the AI agent from the ground up, implementation takes time and a lot of planning.

“When it comes to implementing an AI agent, the major steps include identifying specific use cases, collecting relevant data, developing or selecting algorithms, testing for performance, and finally deploying the agent into production,” says Jason Sherman.

Without the right planning and prep work, your AI agent may not meet your goals — and it could create more frustration than value. In this chapter, we’ll explore the major steps of implementing an AI agent so you know where to start.

1. Assess needs and goals

AI agents are designed to achieve goals and complete tasks. So before you can implement the technology, you have to clearly define the goal the AI agent is expected to achieve. However, to do this, you’ve got to take another step back: Before the goal comes the problem.

What’s the problem your organization is facing that the AI agent will help solve? Is there a faster, easier, or less costly solution than an AI agent? How have you tried to solve the problem already? Why didn’t your solution work? These are important questions to ask.

If you determine that an AI agent is the best way to solve the problem, begin by defining the goal.

Say your organization is inundated with customer questions and issues via social media, and you need a way to triage and respond to them. In this case, an AI agent may be the optimal solution. The goal would be for the AI agent to effectively triage customers on your social media platforms, respond to their questions, and assign customer service reps to talk to specific customers.

2. Select the right AI tools

Which agent architecture in AI is the best choice for your use case? As we discovered in the previous section, there are several different types. Not all of them will be right for your situation, so you have to determine which one will be able to achieve the results you’re looking for.

For example, a simple reflex agent can only complete limited tasks within its environment without accessing other data, while a utility-based agent is designed to achieve a goal based on a specific metric related to the outcome.

In the example of triaging customers on social media, perhaps the best solution is a hierarchical agent that can handle complex requests and assign tasks to lower-level agents based on complexity.

3. Design and build the AI tool

Once you’ve selected the type of AI agent to implement, it’s time to design and build it. The key elements to consider during this stage include

• Defining the functions: What tasks does your AI agent need to complete to achieve its goal? Be sure to define subtasks as well as tasks to complete if there’s an obstacle that’s blocking the agent from completing its goal.
• Designing the interface: Consider who will use the AI agent and their technological skill level. Because the AI agent is autonomous, it won’t require prompts regularly. However, a human will need to monitor the AI agent and ensure that goals are achieved. The interface needs to be user-friendly and easy to learn.
• Preparing the data: What data will the AI agent need to complete its tasks and reach its goal? Will the AI agent have access to additional data if necessary? What sources of data can the AI access? Answering these questions is critical to building the best AI agent for the job.
• Making decisions: Will the AI agent use a rule-based system or machine-learning model to make decisions? What criteria will it need to consider when making each decision? Complex decision trees can help you with this aspect of design.

4. Develop and integrate the AI agent

The development process for custom AI agents involves many complex elements. Here are key aspects you’ll need to pay special attention to during the development and integration process:

• Coding core functions: Start by coding the basic features first, such as how the AI agent will manage data and make decisions. The user interface is another good starting point.
• Modular development: A modular approach to development — separating program functions into independent pieces — is a good strategy, especially for repairs and maintenance. If something goes wrong in a specific module, the entire system won’t be affected.
• Integrations: What other software systems will the AI agent need to connect with to complete tasks? API connections may be necessary for the AI agent to extract and analyze data from other sources. If the AI agent needs to store and collect data, it will also need to be integrated with a database system.
• Memory systems: What kind of memory will the AI agent require? For example, will it need to remember the user’s preferences or how to interact with certain users? If that’s the case, you will need to implement memory mechanisms.
• Testing: When checking to make sure everything works, be sure to test each module individually as well as in combination with other modules. Apply various conditions to make sure the AI agent performs well under different circumstances.
• Documentation: Create detailed user documentation to teach users how to interact with the AI agent. Code documentation for developers is also a good idea in case someone needs to make any adjustments later on.

5. Deploy and monitor the AI agent 

Place the AI agent in a test environment to see how it performs in real-life scenarios. A gradual deployment strategy ensures a smooth rollout without any major hiccups. You could even do a phased rollout, testing the agent with a small user group to gather information and optimize the agent before a full release.

Monitor the AI agent’s performance and collect user feedback to determine whether the software is successful. Consider improvements you can make based on the information you collect, and plan periodic system updates and enhancements to optimize the technology.

AI implementation best practices

Paul Ferguson emphasizes the importance of implementing robust security measures, designing for scalability, and ensuring human oversight: “These practices are crucial for maintaining the agent’s effectiveness and reliability over time.”

Keep in mind that as your business needs evolve, the AI agent should be able to evolve with it.

Sherman adds that it’s best to start small with pilot projects. Other best practices include “ensuring high-quality data for training, incorporating user feedback for continuous improvement, and prioritizing the user experience to make interactions intuitive.” For human users, the AI agent experience should be seamless and natural.

Next up, it’s time to explore some ways AI agents are used in different industries. These use cases can inspire you to implement an AI agent in your organization.

AI agents have the potential to truly transform business operations. They can increase productivity, enhance decision-making, and scale easily. AI agents can also deliver better customer results and improve employee satisfaction, which is always good for business.

“There are numerous use cases for AI agents across various industries,” says Jason Sherman. “For example, in e-commerce, they provide personalized shopping experiences by analyzing customer preferences.” Paul Ferguson adds, “Manufacturers have reduced downtime with predictive maintenance agents.”

AI agents can be used across domains, in any industry. Here are several real-world examples that might inspire you to look for aspects of your business that could benefit from this groundbreaking technology.

AI sales agent

AI agents can transform how sales teams interact with potential customers. They act as always-available assistants, engaging with prospects, answering questions, and gathering key information. By converting contact forms into AI-powered agents, you create a more engaging and efficient experience for everyone.

Customization options let you tailor the AI agent to reflect your brand identity. Adjust the avatar, tone of voice, and form design to create a seamless and professional experience. Embedding these agents on your website provides immediate support to prospects, increasing engagement and potentially boosting conversions.

Essentially, AI agents become tireless members of your sales team, available 24/7 to engage with prospects and guide them through the initial sales funnel stages. This allows human representatives to focus on more complex tasks and qualified leads, improving overall sales efficiency.

AI school administrator agent

Artificial intelligence has many possible roles within the education sector.

“In education, AI tutors are revolutionizing personalized learning,” says Ferguson. They are capable of offering highly personalized recommendations based on students’ learning needs and grade requirements, with the understanding that not all students learn in the same way.

From a school administration perspective, AI agents can be used to track student attendance, communicate weather-related school closures, and assign incoming messages to the appropriate person for response.

They are also useful in creating optimal class schedules that factor in classroom availability, teacher workload, student timetables, and more. School administrators can also use artificial intelligence agents to support grading and writing report cards, answer parent questions, and meet student needs.

AI HR manager agent

AI agents can streamline numerous aspects of the human resources sector. Take the recruiting process, for example. AI agents can comb through resumes and cover letters to identify candidates for shortlisting.

They can also match candidates with specific organizational roles based on factors like experience, education, culture fit, and more. Plus, AI agents can schedule interviews with candidates and send automated reminders and follow-up communications regarding interviews.

AI agents are also being used to support employee communication and engagement. For example, they can answer employee questions about company policies, analyze employee sentiment based on surveys and feedback forms, and provide recommendations to the HR team.

AI agents can even use predictive analytics to determine employee retention rates and offer suggestions for improvement. AI agents can also perform a skills gap analysis, which you can use as a guide for training and hiring.

AI healthcare agent

Healthcare agents can ensure patients feel supported by always being available to answer questions, help fill out forms, and offer guidance regarding financial assistance.

“In healthcare, AI agents assist with scheduling appointments and managing patient data,” says Sherman. Healthcare AI agent tools can also ensure the institution is adequately staffed and manage patient appointments.

Healthcare staff can also use AI agents to facilitate patient and family communication by writing emails and sending automated messages.

AI real estate consultant agent

The real estate industry poses many opportunities for AI agents, as real estate agents tend to wear multiple hats. They handle legal contracts, provide face-to-face customer service, write detailed property listings, and deal with large financial transactions. As a result, there are many areas where AI agents can support the real estate industry.

From an efficiency perspective, they can develop real estate listings, triage customer messages, schedule appointments, and summarize lengthy contracts.

But they can also perform more complex functions that are beneficial to the business. For instance, predictive analytics AI tools can forecast market trends for more informed decision-making. “AI agents help with fraud detection by continuously monitoring transaction patterns and flagging anomalies in real-time,” says Ahmed Elmahdy.

AI tattoo studio manager agent

You may not realize it, but artificial intelligence agents can support a number of operations at a tattoo studio. Tattoo studios can use AI agents to schedule appointments and complete administrative tasks, such as billing, filling out waivers, and following up on missed payments.

AI agents embedded in the studio’s website can answer customer queries and triage customers who need to speak with a human. AI can also be used within the supply chain to ensure the studio always has the necessary supplies in stock.

One unique benefit to tattoo studios: AI agents can generate virtual imagery to show customers how a tattoo idea might look on their bodies. They can also recommend the right type of ink and colors to tattoo artists.

AI summer camp director agent

A tremendous amount of administrative effort goes into planning and organizing a summer camp. AI agents can take some of the load off by assisting with camp programming and analyzing student and parent feedback forms to determine what types of programming they want to see.

AI agents can also develop camp schedules to optimize time and ensure participants get the most out of their experience.

Summer camp directors can use AI agents to field parent calls and messages, offer quick and personalized responses, and triage calls for counselors. These tools are also useful for developing optimal bus schedules that account for multiple pickup and drop-off locations and different arrival and departure times.

AI beauty salon manager agent

Beauty salon managers can use AI agents to improve their personalized recommendations, among other uses. AI tools can scan customer’s faces, analyze skin types, and provide product and service recommendations that consider multiple factors such as pigmentation, beauty goals, and price range.

Plus, there’s the administrative side of beauty salons. AI agents can help managers schedule appointments, optimizing the beautician’s time while still taking care of the customer’s needs. They can also manage the supply chain, ensuring the salon is well-stocked with no backorders.

“Retail businesses have handled a majority of customer inquiries with AI chatbots,” says Ferguson. In the beauty industry, chatbots can field customer queries with ease, freeing up employee time.

AI pharmacist agent

Pharmacies and pharmacists use artificial intelligence agents to support customer engagement, such as with 24-7 chat availability for scheduling orders and handling patient questions. AI agents can also send patient reminders when refills are coming up or ready for pickup.

Using AI agents to minimize repetitive administrative tasks for employees improves outcomes for patients. They could, for example, conduct drug interaction checks to ensure medications are safe for patients to take.

They can also handle inventory management, automatically placing orders so the pharmacy always has what patients need.

AI travel agency agent

AI agents are transforming the way travel agencies operate. For example, agencies can offer travelers personalized recommendations based on their preferences. AI tools can also find optimal routes for trips, taking into account flight prices, layover times, traffic, and weather.

When a language barrier exists, AI agents can step in and offer translations. They offer round-the-clock service, providing recommendations and answering traveler questions whenever they arise. Fraud detection and security is another area ripe for AI agents, as they can help detect payment anomalies and protect traveler data.

There are virtually no limits to how artificial intelligence agents can be used within the workplace — no matter your industry. Once you’ve determined your best use case, the next step is to build one. We’ll explore two different ways of building your AI agent in the next section.

Building your AI agent requires planning, specialized expertise, and a considerable budget if you’re doing it from scratch. Luckily, there’s more than one way to build an AI agent. You can also minimize the effort and cost with prebuilt solutions.

In this section, we’ll give you an overview of the two main options for building an AI agent and show you an intuitive, easy way to create an AI agent with Jotform that streamlines form-related processes.

How to build an AI agent from scratch

Earlier, we covered the major steps of creating and implementing an artificial intelligence agent.

As a reminder, implementing an AI agent involves defining clear objectives, selecting appropriate technologies, designing the agent’s decision-making processes, training it on relevant data, integrating with existing systems, testing rigorously, and ongoing monitoring post-deployment.

If you build your AI agent from scratch, you can go in two directions: Build it in-house with your own resources, or outsource part or all of the development to an external software agency. Regardless of which way you choose, here are some key things to keep in mind:

• You need a team with the right skills. Not all software developers have worked with artificial intelligence technology. It’s important to find skilled developers who are experienced in working with and building AI agents. Of course, skill and experience typically comes with a higher price tag.
• Select the right framework and libraries for the job. Leading technologies such as TensorFlow, PyTorch, and Keras can help you train the AI model to process data and make decisions. The type of tech you need will depend on the goals of your AI agent. For example, you may use different frameworks for a hierarchical agent than for a simple reflex agent.
• Choose the best programming language for your needs. To implement algorithms and access specialized libraries and frameworks, you need to use the right programming language. Many people choose Python since it’s so versatile, but it’s best to evaluate your requirements based on your goals for the AI agent.
• Train your AI agent with high-quality data. Your data should be high quality, error-free, and clean. In addition, the data has to be free from bias. Without the right data, it will be impossible for your AI agent to produce accurate, reliable results.

Now that you understand the basics of AI agents, you’re in a better position to decide how to use them in your organization.

We’ve covered a lot of information in this detailed guide. Here’s a summary of the key points on AI agents that you’ll want to remember going forward.

What you need to know about AI agents

• What is an AI agent? An AI agent is a type of artificial intelligence software that can make decisions and perform actions autonomously in pursuit of a specific goal. Unlike AI assistants, an AI agent doesn’t need to wait for a user prompt to take action. It understands the goal it’s trying to achieve and knows the steps it has to take to achieve it.
• There are many benefits to using an AI agent in the workplace. For one, it can increase your organization’s productivity many times over, quickly completing tasks that take humans much longer. Given access to the right data, AI agents deliver highly accurate and reliable results. They also streamline employee workflows and enhance customer interactions.
• Not all AI agents are alike. There are many types of AI agents, from simple reflex agents (designed to complete a specific, straightforward task, like turning on a light after a certain time), to learning agents (which evolve through sensory inputs and feedback mechanisms, like a complex fraud detection system), and more.
• Building and implementing an AI agent on your own requires skill and expertise. You must work with experienced developers, select the right framework and libraries, and choose an appropriate programming language. Once you’ve built the tool, testing and monitoring are important.
• There are tech-assisted building options. Software tools can help you build your own AI agent, but they involve some coding.
• AI agents have a wide variety of business use cases. AI agents can support essentially any industry — retail, e-commerce, finance, healthcare, and more.

Meet our AI agent guides

Ahmed Elmahdy

Ahmed Elmahdy is the CEO of Rocket Launch Media, a digital marketing agency that uses cutting-edge technology to help businesses grow. With extensive experience in AI-driven marketing solutions, Elmahdy helps clients leverage automation and AI to optimize their digital strategies.

Jason Sherman

Jason Sherman is an award-winning filmmaker, tech startup expert, and cofounder of Vengo AI. With over a decade of experience in AI and entrepreneurship, Sherman helps businesses integrate leading technologies to drive innovation and growth.

Paul Ferguson

Dr. Paul Ferguson is an accomplished AI consultant and founder of Clearlead AI Consulting. He has over 20 years of experience in artificial intelligence, machine learning, and data science. He holds a Ph.D. in AI and a B.Sc. in Computer Applications from Dublin City University. His career spans numerous senior roles in Fortune 500 companies, research centers, and startups across healthcare, telecommunications, and consumer electronics.