How to Build AI Agents for Beginners: A Step-by-Step Guide

Written by:

Reviewed by: Regal Product Team

Published

Feb 14, 2025

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Last updated

Mar 17, 2026

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5 min read

So, you’re looking to invest in AI agents for sales and CX.

But, you need something a little more intelligent than the average GPT. More purpose-built for your customers. So you find yourself asking…

“How do I build an AI Agent? Is it going to cost millions of dollars to get live? What can AI agents even do for me right now?”

Don’t worry. The good news is, you have options. AI agents and Voice AI are getting genuinely really good—sounding ridiculously human, without having to cost an arm and a leg.

The bad news is, the AI landscape can be hard to navigate. As an AI Agent company, we’re aware of how much BS is being marketed to you…

Ohhh, the AI-rony.

But, we really do get it. Some options are very difficult to implement. Some are way overpriced. Many have agents that just flat out stink.

Whether you have coding knowledge, or none at all, there are options that make it simple to spin up customized, very human-like AI agents.

Use this article as your starting point. We'll explore how to build AI agents for beginners, define what an AI agent can actually be in 2025, look at what you need to consider when building your first agent, and how to start tackling the process.

What do we even mean by “AI Agent”?

When most people think of an AI agent, they think of Siri or Alexa. Perhaps, in a more advanced sense, they’ll think of ChatGPT or Google Bard.

These, however, are NOT AI agents. They are AI voice assistants and chatbots. Very big difference.

AI agents are purpose-built. They are designed to carry out a specific, deliberate interaction, to achieve a specific result.

As defined by Webster, an “agent” is:

“one who is authorized to act for or in the place of another”
“a means or instrument by which a guiding intelligence achieves a result”

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So, AI agents 1) are authorized to act on your company’s behalf. Built by you, for you. Additionally, they 2) serve as a guiding intelligence for your customers, to both achieve a result per every conversation, but also help your customers reach their desired overall outcomes.

Siri, Alexa, and even ChatGPT are designed to answer general, purpose-agnostic questions.

When we say AI agents, we mean:

An agent that works for an auto insurance company that gets in touch with policyholders and helps them renew their plans.
An agent that proactively reaches out to patients to remind them to take their medicine.
An agent that sends reminders to homeowners when a service hand is on the way.
An agent that signals new bootcamp courses of interest for registered students.

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This only scratches the surface of what AI agents are able to do on a 24/7 basis.

Types of AI Agents

Here's a breakdown of the full spectrum of available chatbots, voice assistants, and proper AI agents:

Chatbots, Voice Assistants, and other Workflow Agents might be considered…

Simple Reflex Agents – These agents operate based on predefined rules and scripts, responding to inputs with fixed outputs—a basic chatbot that replies according to a set script.‍
Model-Based Agents – These agents are more intake focused, building and maintaining an internal model of an environment, updating information that you can use in CX outreach.
‍Goal-Based Agents – Goal-based agents are less intake focused, and more action focused, designed to make a series of decisions that lead to specific objectives. They dynamically adjust their behavior and decision-making processes to stay on track toward reaching their predefined goals.

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Gen AI Agents

Gen AI Agents are the next level of AI agents. They combine all of the agent functionalities mentioned above, across intake, output, and outreach.

They’re designed to mimic human-like traits, including personality, decision-making, and communication. They act like real people, engaging with users in a more natural, intuitive way, far beyond the capabilities of traditional voice assistants like Siri or Alexa.

What’s also worth mentioning, is that, because of the generative nature of the technology, it can be carried out in limitless workstreams—i.e. as many agents as any CX team could need or want, all acting at the same time.

Real-World AI Agent Examples

When we think of the key functions of AI agents across sales and CX, there are a number of ways they can operate self-sufficiently:

General Customer Support: Of course. BUT, AI agents handle general FAQs and resolve common issues 24/7/365, but do so as a conversation, instead of just sending an FAQ link that may or may not point to the right answer.
Lead Qualification: Across many business types, AI agents can qualify leads by engaging with them, asking qualifying questions, and then routing them to the right agent or workflow.
Scheduling & Reminders: AI agents manage appointment bookings, send reminders, updates, and follow up on engagements.
Patient Engagement: AI agents facilitate appointment scheduling, send reminders, and answer common health-related questions.
Health Program Adherence: AI agents manage follow-ups, treatment and medication reminders, and check-in communications with patients.
Student Enrollment & Reminders: AI agents handle student inquiries, assist with enrollment processes, and send reminders for deadlines, events, and new courses of interest.
Course Assistance: AI agents offer personalized study recommendations, track academic progress, and answer academic queries.
Insurance Policyholder Communications: AI agents handle inquiries about policies, provide updates on claims, and send renewal reminders.
Home Improvement Scheduling and Reminders: AI agents coordinate appointments for services like plumbing, electrical work, and remodeling. Agents follow up with customers, send reminders, and send live service updates.

How to Build AI Agents for Beginners: Questions and Considerations

The way you actually build your AI agent will depend on your business instance. Below are the questions and considerations you should keep in mind to select the right tools and service partners and get the process started.

What to ask yourself when building an AI agent for sales and CX

What problem will your AI agent solve?
Begin by defining the purpose of your AI agent. Do you have very simple outbound volume goals you’re trying to hit? Or are you looking for more reflexive, responsive agents to help customers through personalized journeys?
What data will you use to train your AI agent?
What are you currently tracking? How much do you actually know about your customers? Do you simply need to plug AI agents into an existing database, or do you want to start collecting more data as part of implementing AI agents?For example, platforms like Regal centralize your first-party customer data and integrate that data directly into the desktop your AI and human agents work out of—for visibility, and to inform how you continue to build out and test customer journeys.
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Which AI agent builder will you use?
Do you need a full no-code builder? Do you want to integrate your agents directly into your CCaaS desktops and workflows? How much technical support do you have internally?
How will you test and optimize your AI agent?
A/B testing is critical to refining performance. Do you want a platform that has A/B testing natively built into agent workflows?
How will you measure the efficacy of your AI agents?
Do you have ready-made dashboards to measure the efficacy of your AI agents (versus human agents, across different channels and iterations)?

More holistic AI agent tooling will also offer built in reporting along with AI agent functionality.
How will you address data bias and ensure ethical AI?
AI models can unintentionally reflect biases from their training data, which could lead to unfair or unethical outcomes.

Are you confident in your team’s technical ability to set and update the necessary guardrails to assure ethical outcomes, HIPAA compliance, or any other strict biases?

Regulated outreach requires more sophisticated AI agents and platforms.
How will you handle errors and debugging?
AI agents may generate incorrect or unexpected responses. Is your team equipped to respond quickly and make updates in real-time? Do you have the data at-hand to react in real-time?

With these questions in mind, you should have a starting point for the kind of AI agent builder you need to get moving.

Skills to Build an AI Agent

Let’s say you were going to be hands-on and build your agent in-house. Here’s an overview of the essential skills to build an ai agent for both coding-based and no code AI agent builders, perfect for anyone thinking about how to build AI agents for beginners.

Coding-Based AI Agent Development Skills

Programming Languages: Python, JavaScript, or Java are commonly used to develop AI agents. Python, in particular, is favored for its rich libraries and frameworks in AI.
Machine Learning (ML): A strong understanding of both supervised and unsupervised learning is crucial for building agents that can make decisions based on data.
Natural Language Processing (NLP): This is essential for AI agents that handle text or voice-based interactions, enabling them to understand and generate human-like communication.
Data Labeling & Preprocessing: AI agents require high-quality data for training, and effective data preprocessing ensures the agent learns from the best inputs.
API Integration: Connecting an AI agent with external databases and third-party applications is often necessary for creating a fully functional and responsive agent.

No-Code AI Agent Builder Skills

Using AI Agent Builder Platforms: Platforms like Regal allow you to build AI agents using visual interfaces, making it easy to set up workflows and deploy agents quickly.
Understanding Workflows: A basic understanding of how AI agents process information and execute tasks will help you design and test better workflows.
Basic Logic & Decision-Making Rules: Configure your agents to respond to user inputs with predefined actions and logic, making them more efficient and effective in handling real-world interactions.

Tools & Platforms for AI Agent Development

The right tools can make a huge difference in how effectively you can build and deploy your AI agent. Whether you're comfortable with coding or prefer a more user-friendly, drag-and-drop approach, the following platforms cater to both preferences.

Code-Based AI Agent Builders

OpenAI API – A robust API that powers AI agents with advanced natural language understanding, such as GPT-based models.
LangChain – A flexible platform that allows AI agents to integrate seamlessly with external tools and memory modules.‍
AutoGen – A comprehensive agent framework for building automated workflows that can scale and adapt to various business needs.

No-Code AI Agent Builder Options

How No-Code AI Agent Builders like Regal Work:

Drag-and-Drop Interfaces: These platforms simplify agent creation by allowing you to define workflows visually, without writing code.
Pre-Trained AI Models: Many no-code AI agent builders come with pre-built models and options, so you don’t need to train AI from scratch.‍
Integration: Automatically integrate with your customer data, CRM, CCaaS, and other relevant tools.

Future Trends

Gartner has long stated that by 2030, AI-powered agents will handle 80% of business automation.

At Regal, we believe that trend will continue—90% of interactions will be automated by 2035. And most of that will be voice.

Some current trends to keep in mind as you build your AI agents:

More Intelligent AI Agents – AI will continue to handle more complex decision-making. And advancements happen very quickly. If you don’t adopt AI agents now, you’ll continue falling farther behind more and more quickly as they evolve.
No-Code Expansion – AI development will become more accessible for non-technical users.
Multimodal AI Agents – Future agents will understand text, voice, and images, and be able to respond accordingly to each.

Overall, what’s most important is simply getting started.

The sales and CX teams that learn how to build AI agents for beginners and integrate automation the soonest will continue to have a major cost advantage—and will likely also have a major customer experience advantage.

Frequently Asked Questions

How is an AI agent different from a voice assistant or simple chatbot?

AI agents are purpose-built to carry out specific interactions that achieve a defined outcome, while voice assistants and simple chatbots typically follow scripts or handle generic Q&A without goal-directed behavior.

What decisions should I make before starting to build an AI agent for sales or CX?

Define the problem to solve, determine the customer data the agent will use, choose a code-based or no-code builder, plan A/B testing, set up measurement dashboards, establish ethical/compliance guardrails, and prepare a process for error handling and updates.

Can I build an effective AI agent without coding?

Yes. No-code builders let you design workflows visually, use pre-trained models, and integrate with systems like CRM and CCaaS without writing code.

Which code-based tools or frameworks are recommended for building AI agents?

OpenAI API can provide language capabilities, LangChain helps integrate tools and memory, and AutoGen supports building scalable, automated multi-agent workflows.

How should I test and measure my AI agent’s performance after launch?

Use A/B testing to compare agent variants and refine behavior, and track results with dashboards that benchmark outcomes across channels and versions, including comparisons to human agents.

Founded in 2020, Regal is an enterprise voice AI agent platform for contact centers. Regal helps businesses build, deploy, and manage autonomous AI agents across sales, support, and operations teams.

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Get behind-the-scenes insights about Perry Health’s 23% revenue increase with Regal.io. Perry Health’s Scott Chesrown sat down with Regal.io and discussed how they implemented outbound sales technology, integrated new capabilities into their process, and built on their early success.

Drive More Contact Center Revenue With These 6 Holiday Tips

It’s the holiday season and you’d like to hit your December revenue goals. Outbound B2C sales during the holidays can be a major performance driver – if you're prepared and you have the right tools in place. Use these seven tips to get your outbound B2C sales in shape for holiday success.

Regal.io Raises $38.5M in Series A Funding

Today we are excited to announce that based on the success our customers are seeing using Regal.io, we have raised $38.5 million in Series A funding led by Emergence Capital to continue to invest in our teams and products.

Announcing our Integration with mParticle

Our integration with mParticle enables you to create a unified customer profile and connect other first party customer data.

What is a Journey Builder?: A Complete Guide

Say goodbye to batch processing, complicated SQL queries or custom engineering to decide who to call. Journey builders help B2C create more timely, relevant and personalized outreach to customers.

Goodbye Regal Voice, Hello Regal.io

Friends and supporters, We are excited to announce our updated branding and website. It better captures what we stand for.

Announcing our Integration with Twilio Segment

We are excited to announce our integration with Twilio Segment, allowing you to seamlessly connect your first-party customer data with Regal.io and 200+ other integrated tools.

Milestone: $1 Billion Revenue Driven for Our Customers

We are thrilled to announce that Regal.io has driven over 20M conversations between our customers and their end users, leading to $1 billion in revenue driven to date.

The RAG Playbook: Structuring Scalable Knowledge Bases for Reliable AI Agents

Learn how to structure a knowledge base that keeps AI Agents accurate and on-script. Discover why human-style KBs fail, and apply best practices like single-topic chunking, concrete instructions, and explicit outcomes to reduce hallucinations and scale reliable RAG performance.

How to Use SIP to Integrate Regal Voice AI Agents with your Contact Center Software

Learn how SIP integration enables enterprises to connect Regal Voice AI Agents to existing CCaaS platforms, pass call context in real time, and deploy voice AI without infrastructure changes.

Demystifying AMD: How Answering Machine Detection Really Works

Regal's advanced AMD algorithm uses multiple factors to determine if a human answers your call. Learn more about the different techniques available.

February 2026 Releases

In February, we made AI agents sound and feel more natural, even when handling complex, multi-step conversations with conditional logic. These updates give you the control to build AI agents that don't just follow a script: they adapt, respond, and engage naturally with each customer.

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