AI agents are changing the game in today’s business landscape. The AI agents market is already estimated to be valued at $7.38 billion this year, and is projected to reach $47.1 billion by 2030.
Source: SellersCommerce
And it’s not just about monetary value.
- 34% of consumers prefer working with an AI agent instead of a person for faster service.
- 32% of Gen Z consumers are comfortable with an AI agent shopping for them.
Businesses are understanding the value that generative AI tools add to the customer experience, and are wanting to hop on to training their own agents. Whether it’s automating tasks, improving customer interactions, or making operations more efficient, these digital assistants are helping businesses level up.
Keep reading for a step-by-step guide on how to build an AI agent for your business.
What is an AI Agent?
An AI agent is essentially a software tool that can help people by performing tasks and using data to make decisions. Although human touch may still be required, it can largely operate on its own and improve itself over time.
AI Agent Applications in Business can Include:
- Customer Service Enhancements: Assist customers 24/7 with chat support, answer questions, and personalize their shopping experience.
- Automate Repetitive Tasks: Handle emails, scheduling, data entry, or any other tasks that become repetitive.
- Analyze Data & Insights: Process large amounts of data to forecast trends and predict consumer behaviors.
- Optimize Operations: Manage inventory, track orders, and streamline workflows—acting as a project management tool.
In short, an AI agent is like an extra set of hands whose purpose is to help your team make business decisions faster and provide more personalized solutions for your customers.
How to Build an AI Agent for Your Business
Creating an AI agent may come off as complex, but with the right tools, it is easily manageable. Building an AI agent requires training it to understand and respond to human communication in a way that is helpful and resourceful. Whether you’re aiming for a simple chatbot or a sophisticated automation system, the process follows a clear path.
So, where should you start?
Define Your Business Goals
You’ll need to identify which problems you need the AI agent to solve. Maybe your team needs help with an overwhelming amount of customer service questions, or you need a quicker way to retrieve important insights from a huge data collection—these are things that can be done efficiently and effectively with an AI agent.
Here are some parameters you should define before building your AI agent:
Identity Pain Points and Challenges
Ask what tasks are repetitive, time-consuming, or prone to human error? Are customers facing issues like long wait times or inconsistent service? Are there roadblocks in operations that slow down efficiency?
Set Tangible Objectives
Do you want to reduce response time in customer service? Do you need better data analysis to improve decision-making? Do you want to increase sales by automating lead generation?
Define Success
What’s most important to your team? Tracking response time and satisfaction scores? Optimizing marketing and measuring conversion rates and engagement? Reducing costs through savings and automation?
Ensure an AI Agent Aligns with Your Business Strategy
AI should complement business goals, not replace human expertise where it’s most valuable. Make sure your AI agent can integrate seamlessly with existing tools and workflows.
Data Collection and Preprocessing
Now that you’ve defined your business goals, the next step is data collection and preprocessing—because creating an AI agent will only be as good as the data it learns from.
An important thing to remember is that AI does not “think” like a human; it learns from data patterns. If an AI agent is being used for customer support, sales forecasting, or automation, it needs high-quality, relevant, and up-to-date data to function effectively.
First, you need to collect relevant data sources such as:
- Customer questions, conversations, and support tickets
- Invoices, sales data, and marketing analytics
- Internal documentation, policies, and manuals
Then you’ll need to clean out and structure your data by:
- Removing outdated information, deleting duplicated documents, and irrelevant data
- Ensuring compliance with data privacy and security, especially for sensitive customer data
- Standardizing formats within your documents like text fields, dates, and categories
Once these steps are completed, you can then pull in a knowledge base tool to build your AI agent specifically for your business.
Our favorite technique to do this? Retrieval-Augmented Generation.
Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is a strategy that is used to enhance AI agents with business knowledge by enabling the AI to pull information from live, relevant data sources—like your company’s knowledge base, FAQs, internal reports, or CRM system.
How RAG Works:
- Retrieval: The AI searches through stored documents, databases, or real-time sources to find the most relevant information.
- Augmentation: The AI injects this data into its response, ensuring it provides the most accurate and up-to-date answer.
- Generation: It formulates a natural language response using both its foundational knowledge and the retrieved business-specific information.
RAG is an incredible tool to help prepare your data because AI pulls the latest and most relevant information, understands your industry-specific needs, and ensures that the AI follows company policies and uses verified data.
Overall, Generative AI tools can only successfully optimize your business with the right data to train with. By using clean and structured data along with implementing RAG, your AI agent will become a valuable asset to your company—delivering relevant, real-time, and accurate information that improves operations, customer experience, and decision-making.
Choose the Right Tools
Now that you’ve defined your business goals and prepared high-quality data, the next step is selecting the right tools and platforms to build your AI agent. Don’t worry—you don’t have to build everything from scratch. Choosing the right AI tools will help your AI agent to work fast and cost-effectively.
So, what are the tools needed to create an AI agent?
Types of AI Tools for Business Owners
Pre-Built AI APIs
These are quick and efficient solutions with no coding required that help integrate AI capabilities without having to create an AI agent from scratch. Pre-built APIs are useful tools for customer support, automation, and data analysis.
We recommend:
- OpenAI’s GPT-4 API: Great for advanced natural language understanding and chatbots.
- Google Cloud AI: AI-powered tools for document processing, translation, and analytics.
- Amazon Bedrock / AWS AI Services: Provides AI-powered tools for customer engagement, fraud detection, and automation.
Pre-built APIs work best for businesses that are looking to quickly integrate AI into their websites, apps, or workflows without having deep AI expertise.
Low-code/No-code AI Platforms
If you’re looking for AI tools that have faster customization capabilities but don’t want to write complex code, these are ideal for you—especially if your business is small to mid-sized.
We recommend:
- ChatGPT Enterprise/Open AI Assistants API: Perfect for building customized AI agents.
- Dialogflow by Google: Create conversational AI chatbots and voice assistants for natural language flow.
- Hugging Face Transformers: Great for deploying pre-trained AI models with very minimal coding.
Source: OpenAI
These tools are here for you so you don’t need to hire a full AI development team, providing you with some customization capabilities.
Advanced AI Frameworks
If your business needs full customization and AI model training for deep learning, predictive analytics, or AI-powered automation, you’ll need to utilize a more advanced framework.
We recommend:
- TensorFlow & Keras: Keras is the high-level API of the TensorFlow platform, providing open-source deep learning for image recognition, forecasting, and automation.
- PyTorch: This is preferred for AI research and custom machine learning models.
- LangChain: Builds AI agents that interact with business data using RAG.
These tools provide highly customized AI solutions for business needs such as financial predictions, AI-powered automation, and industry-specific solutions.
The Bottom Line for Choosing the Right Tools
It all comes down to the needs of your business, budget, and technical expertise. To sum it up, Pre-built APIs are going to be the fastest route but have a limitation on customization. If you have some coding knowledge, a low-code API has faster customization capabilities while being ideal for a small to mid-size business. For a wider range of capabilities that do require advanced knowledge to input, you should consider advanced AI frameworks.
Beginning the Development Phase
So, you’ve defined your goals, collected data, and have your tool of choice to begin building an AI agent. Now comes the fun part—developing your AI agent! This phase deals with designing how the AI will process information, respond to users, and continuously improve over time.
There are three components to this phase:
- Natural Language Processing (NLP)
- Training & Fine-Tuning
- Testing & Evaluation
Natural Language Processing
NLP helps AI to understand and respond to human language, detect intent, and generate relevant responses. This is what personalizes user experience and makes it easier for customers to engage with an AI.
Some NLP technologies include:
- Transformer Models (GPT-4, LLaMA, Claude): Great for advanced language comprehension.
- SpaCy & NLTK: Enables text processing and entity recognition.
- Speech-to-Text (Whisper, Google Speech API): If voice interaction is needed.
Source: GeeksforGeeks
An AI agent use case for NLP could be a chatbot that understands customer inquiries and is able to respond naturally, sounding less robotic.
Training & Fine Tuning
If you haven’t figured it out by now, the best AI agents will come from providing recent and specific data to create relevant and business-specific responses. Training your AI agent is essential for it to become an asset to your business.
How to train your model:
- Collect & Clean Data: Use structured data sets like customer chats, FAQs, or historical reports—just like when you began the process of adapting an AI agent.
- Choose a Base Model: Use a tool like GPT-4 for general chatbots or a customized model for more specialized AI.
- Fine-Tune with Business Data: Use domain-specific examples to adjust the AI agent.
- Test & Improve: Refine the AI continuously based on user interactions and feedback.
Testing & Evaluation
This is a simple step that ensures AI accuracy. Always make sure to test out the AI agent before launching to see how well it understands and responds to inquiries.
Some key testing areas are:
- Accuracy: Is the agent able to provide correct and reliable information?
- Bias & Ethics: Is the AI free from biases and appears neutral?
- Performance: Can the agent handle multiple inquiries fast and efficiently?
- User Experience: Does the agent feel and respond naturally and helpfully?
If you’re implementing a customer support chatbot, test it against real questions that are relevant to your business to ensure it resolves issues effectively and naturally.
The Developmental Goal for Business Owners
This phase is where your AI agent really starts to become an asset to your business. Focusing on NLP, training, and extensive testing ensures that your AI agent is not only reliable and intelligent but is aligned with your business goals.
Deployment & Maintenance
The final step to building your AI agent is deployment—getting into real-world use. However, deployment is not a one-and-done process. You’ll need to monitor, update, and optimize it constantly to ensure long-term success for your company.
After you have tested your AI agent and it is ready for deployment, choosing the best way to launch it will be directly based on your business model and needs.
Here are some deployment options:
- Cloud-Based Deployment: Choose a service like AWS, Azure, or Google Cloud for scalable AI hosting.
- On-Premise Deployment: If your business needs higher security for things like finance and healthcare, check out this option.
- Integration with Business Tools: Connect your AI agent with CRM, websites, mobile apps or help desks.
Deploying your AI agent is not the end of building an AI agent—it’s the beginning of continuous optimization. Your AI agent will stay efficient, accurate, and valuable by monitoring its performance, retraining its models, and ensuring security.
Applications of AI Agents
With all of these steps in mind, you may be wondering what the use cases for an AI agent are and why it is so important to integrate into your business. AI agents can revolutionize your business by improving efficiency, automating tasks, and personalizing customer experiences.
Let’s take a look at three areas where an AI agent can deliver value to your business.
Customer Service & AI Chatbots
These AI-powered virtual assistants change the game for customer service. They are able to handle questions, provide personalized assistance, and escalate complex issues to humans—all while improving response time and reducing operational costs.
Source: Anytime Digital Marketing
Here’s how AI agents can improve customer support:
- 24/7 Availability: Unlike people, an AI agent is always online and can support your customers when needed at any time of the day or night.
- Instant Responses: Chatbots can answer FAQs, guide customers through problems, and troubleshoot issues.
- Multilingual Support: AI can communicate or translate multiple languages, providing better service to customers globally.
- Seamless Handoff: If an issue is too complex or a person would like to speak to another person, AI can route them to the right person.
- Personalized Interactions: AI agents can analyze customer history to provide tailored and personalized responses.
With that in mind, what is a use case for this kind of service? Here’s an example.
If you’re an e-commerce company that deployed an AI chatbot on your website, you probably want it to handle order tracking, refunds, and recommend products. By using a chatbot, you’re able to reduce customer wait times and improve engagement.
That leads us into the next area:
Lead Generation & Qualification Marketing
AI agents can automate marketing tasks, helping businesses generate more leads, qualify prospects, and optimize marketing campaigns.
Here’s how:
- Ad Optimization: An AI agent can analyze data to improve pay-per-click and social media targeting.
- AI-Powered Lead Scoring: The agent analyzes consumer behavior and ranks leads based on conversational potential.
- Automated Outreach & Follow-Ups: AI can personalize emails and send them at the right time.
- Dynamic Personalization: An AI agent tailors marketing messaging based on customer interest.
- AI Chatbots for Lead Capture: Chatbots engage website visitors, collect information, and qualify them as potential customers.
An example use case here could be a real estate agency using an AI agent to analyze website visitors’ behavior and then automatically reach out with personalized property recommendations. A chatbot could then collect user preferences and schedule viewings.
Finally, we have the last area where an AI agent can help your business benefit:
Automating Repetitive Administrative Tasks
AI agents can reduce and eliminate time-consuming administrative tasks, granting more time to employees to focus on high-value work. Automation improves efficiency, reduces errors, and cuts costs for your business to be successful in the long run.
How AI automates administrative work:
- Email Management: AI filters emails, drafts responses, and prioritizes urgent messages.
- Data Entry & Report Generation: AI extracts, organizes, and processes data automatically.
- Invoice Processing & Expense Tracking: AI scans and categorizes expenses for finance teams.
- HR Automation: AI streamlines employee onboarding, time tracking, and payroll processing.
- Scheduling & Calendar Management: AI assists with booking meetings and avoiding scheduling conflicts.
An AI agent use case for this area could be a law firm that integrates an AI agent to automate document processing or summarizing contracts. This saves employees hours of manual labor, allowing them to focus on valuable work and their clients.
Key Considerations for Developing an AI Agent
At this point, you may be getting excited about the opportunity to build an AI agent and integrate it into your business. However, there are still key things you need to consider when creating one.
We’ve outlined the key things you should consider when developing an AI agent and the answers to some of the questions you may be wondering down below.
How Much Does Building an AI Agent Cost?
Before building an AI agent, you need to consider the costs and resources required. The investment varies depending on factors such as custom development vs. pre-built solutions, hardware needs, and ongoing maintenance.
Key budget considerations include:
- Development Costs: Hiring AI engineers vs. using AI-as-a-service (APIs).
- Infrastructure Costs: Cloud computing, servers, storage, and databases.
- Training & Fine-Tuning Costs: Data collection, annotation, and model retraining.
- Integration Costs: Connecting AI with existing business systems (CRMs, ERPs).
- Ongoing Maintenance: Regular updates, monitoring, and compliance audits.
If you are a mid-sized business, you could spend anywhere from $10,000 to $50,000 developing an AI agent due to the level of customization, security, and performance needed to scale. However, if you use a pre-trained AI API, you could end up spending as little as $100-$1,000 per month on development.
Security & Ethical Considerations
AI agents handle sensitive business and customer data, making security and ethics a top priority. Businesses need to prevent data breaches, unauthorized access, and AI biases to ensure compliance and fairness.
Source: Codementor
For example, a healthcare AI agent must comply with HIPAA regulations to ensure that patient data remains confidential and unaffected by biases.
Here are the security and ethical considerations to remember:
- Data Privacy & Compliance: Follows GDPR, CCPA, HIPAA, or other industry regulations.
- Secure Data Storage & Encryption: Encrypts sensitive data and controls access permissions.
- Bias & Fairness Testing: Ensures AI doesn’t discriminate based on race, gender, or other biases.
- AI Explainability & Transparency: Makes the AI decision-making processes understandable.
- User Consent & Data Usage Policies: Clearly informs users how AI is processing their data.
In order to ensure these considerations are met, you may want to utilize privacy-preserving AI techniques to protect user data, regularly audit your agent for biases, and implement role-based access control to restrict access to sensitive information.
Rigorous Testing for Bias & Performance
AI agents need to be thoroughly tested before deployment to prevent inaccurate, biased, or misleading responses. Without proper testing, AI can amplify biases, misinterpret data, or fail in real-world applications, leading to negative outcomes for your business.
Some key testing areas include:
- Bias Detection: Test AI on diverse datasets to ensure fair treatment across demographics.
- Performance Accuracy: Measure AI’s ability to provide correct and relevant responses.
- Stress Testing & Scalability: Ensure AI can handle high traffic and complex requests.
- Edge Case Handling: Train AI to recognize and properly handle unusual inputs.
- User Feedback Loop: Allow customers and employees to report AI errors for ongoing improvement.
The Future of AI Agents in Business
AI agents are evolving at a rapid pace and influencing various industries, including marketing. Right now, 82% of global industries either use AI or are exploring the opportunity of using AI in their businesses.
Being able to understand current trends in generative AI tools and recognizing other industries that are leveraging AI agents is crucial for scaling your business and retaining a competitive edge.
Here are some trends for you to consider and understand how to build an AI agent tailored to your business.
Hyper-Personalization
AI agents are enabling businesses to offer highly personalized experiences by analyzing vast amounts of data to tailor products, services, and content to individual preferences. This trend is not only improving customer satisfaction but increasing engagement as well.
Conversational AI
NLP has made large strides in sophisticating AI agents. These agents are able to understand and engage in human-like conversations, leading to improved customer service and support efforts.
Multi-Modal AI
AI agents are increasingly integrating multiple types of data including text, images, and audio to provide more human-like outputs.
AI Ethics and Regulation
As AI has become more integrated into our lives, there is a growing emphasis on ethical responsibilities and regulations to ensure responsible use and lack of biases in its technology.
Predictive Analytics
AI agents can analyze historical data from consumers and predict future trends. This is helping businesses to strategically plan and make informed business decisions.
Keeping up to date with these trends and implementing them into your AI agent is key to driving innovation and scalability within your organization.
Emerging Industries Leveraging AI Agents
What industries use AI agents the most?
- Healthcare: Agents are assisting in diagnostics, personalized treatment plans, and patient monitoring.
- Finance & Banking: Fraud detection, risk assessment, and customer service are improved through the use of an AI agent.
- Retail & E-commerce: AI agents are predicting shopping trends, managing inventory, and optimizing pricing strategies.
- Education: AI is automating administrative tasks, providing intelligent tutoring systems, and personalizing learning.
- Agriculture: AI agents are aiding in crop monitoring, soil analysis, and predictive analytics.
Industries all around the world are implementing AI agents into their businesses to improve efficiency and user experience. These examples are all industries with large amounts of money and functionality to integrate AI technology into their businesses.
So, can small businesses benefit from building AI agents?
In short, yes! AI agents assist small businesses in a key aspect: cost reduction. AI agents help your small businesses reduce the need for extra hands doing routine tasks. An AI agent also helps to scale your small business by adapting to handle more customers and improve CRM. With Marketwake’s CRM expertise and partnership with Hubspot, you can leverage your small business’s revenue and profitability.
Leveraging AI Agent Applications in Business
Your business needs to create an AI agent. They are no longer a luxurious tool reserved for large enterprises, but an essential tool for businesses of all sizes to grow, streamline operations, enhance customer experiences, and aid in decision-making. Businesses that invest in an AI agent stay competitive, optimize resources, and future-proof their operations.
Ready to incorporate AI into your business? Let our team help you design and implement a custom AI solution.
On top of an AI agent, if you need a marketing partner, Marketwake offers performance marketing, CRM services, and creative solutions that help your brand grow and stand out.
Contact us today!
Frequently Asked Questions
Can AI Agents Replace Human Workers?
Although AI agents can help automate many tasks, they are not a complete replacement for human workers. As AI struggles with emotional intelligence and lacks the human spirit, it is always important to use AI agents as a resource, not a replacement.
Are AI Agents Secure and Compliant with Regulations?
Yes, but it all depends on how they are built, deployed, and maintained. It is ultimately up to businesses to ensure best practices and protect data privacy, security, and compliance. The National Institute of Standards and Technology (NIST) provides rules and regulations on AI as well.
How Long Does it Take to Develop an AI Agent?
The time it takes to develop an AI agent varies on several factors such as complexity, type of AI involved, resource availability, and the specific use case. Simple AI agents can take anywhere from two to four months, whereas advanced AI agents can take up to 6 months to a year.
