How to Build an AI Agent

In this blog article, I will provide a step-by-step guide on how to build an AI agent as well as discuss AI agent successful use cases, benefits for business, and underlying technologies. Let’s dive right in!

In this article

1. What Is an AI Agent?
2. AI Agent Use Cases
3. Why Develop a Custom AI Agent — Strategic Advantages for Businesses
4. How to Build an AI Agent: Essential Steps
5. Real-Life AI Agent Examples
6. How DevTeam.Space Can Help You Build an AI Agent
7. Frequently Asked Questions on How to Create an AI Agent

What Is an AI Agent?

An AI agent is a software program that uses advanced AI models, often having generative AI capabilities, to achieve set goals. AI agents are able to make decisions, take actions, and assist people by interacting with different tools, SaaS applications, colleagues, and even other AI agents. 

So, how is an AI agent different from traditional automation tools? 

Unlike traditional software automation tools that only perform pre-coded tasks, AI agents can perform tasks on their own, using algorithms and data to make decisions independently. At the current state of technology, you can definitely think of AI agents as your new class of employees. They are good enough already. And will become even better within the next 2-3 years. 

What’s more, AI agents make rational choices. They learn from experience and human feedback, analyze data to predict the best outcomes, and make informed decisions.

AI agents are being increasingly adopted across industries as they help businesses boost competitiveness, increase efficiency, and drive faster and stronger growth. 

Iconic companies like Goldman Sachs and Mercedes Benz are using generative AI in their business practices, and enterprise spending on generative AI reached $13.8 billion in 2024.

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Let’s discuss possible use cases for AI agents and see how you can leverage agentic AI for your business. 

AI Agent Use Cases

AI agents can be assigned a variety of tasks, from handling simple repetitive actions like composing emails to data-driven decision-making like sorting out potential leads, doing the initial email outreach, and even responding to emails without pre-set templates but rather based on context.

What Tasks Can AI Agents Perform?

Enterprise-grade AI agent applications encompass a wide range of industries and business sectors.

1. Healthcare (analyze medical images, analyze electronic health records and suggest possible diagnoses, predict risks of specific conditions in patients, predict disease treatment outcomes, schedule appointments, enter medical data, transcribe doctor-patient conversations, etc.)
2. Sales (score and qualify leads, use predictive analytics to forecast sales and identify trends, compose personalized emails, schedule meetings, perform market and competitor analysis, predict the effectiveness of marketing campaigns, generate reports, track KPIs, etc.)
3. Manufacture (suggest new product designs, optimize existing products, test virtual prototypes, adjust manufacturing processes, predict equipment failures before they occur, perform quality control, manage inventory, etc.)
4. Agriculture (monitor crop and livestock health, make data-driven irrigation and fertilizing schedules, control pests and weeds, predict yields, manage planting and harvesting, etc.)
5. HR and Recruitment (write job descriptions, post job adverts, review incoming resumes, shortlist candidates, analyze video interviews, onboard new hires, develop corporate training programs, conduct performance reviews, etc.)
6. Financial Services (respond to customers’ questions about account balances, transactions, loan terms, insurance policies, etc., monitor transactions to detect fraud, generate personalized financial recommendations, make individual and corporate customers’ credit profiles, assess risks, etc.)
7. Software Development (generate, refactor, and review code, detect vulnerabilities, generate automated tests, generate technical documentation, etc.)

Since agentic AI is still a relatively new technology, you’ll likely be stepping into unchartered territory with your project, pioneering the introduction of a new use case for AI agents.

To make this pioneering use case a reality, you’ll need to build a custom AI agent tailored to your specific needs.

Why Develop a Custom AI Agent — Strategic Advantages for Businesses

To skim the cream off the booming GenAI market, many startups and established companies offer ready-made AI agents or AI agent frameworks for those who don’t want to build from scratch. 

You can try such out-of-the-box solutions. However, before you waste your time on a cookie-cutter AI agent platform, consider the advantages of building a domain-specific, custom AI agent that can truly get your business to the next level.

Custom AI Agents' Advantages

1. Customization for specific business needs. Custom AI agents can solve specific problems unique for your business sector, company, or business process.
2. Higher productivity. The size of initial investments can be scary, but in the long run, a tailored product will save money by automating tasks.
3. Better integration with existing systems. Large enterprises have multiple software applications for customer, employee, and process management. These applications contain huge amounts of data. Custom AI agents can be easily integrated with these existing systems.
4. Scalability. AI agents built specifically for your company can scale as your business grows. You can add new features and modules at your discretion without the limitations of AI agents created with AI agent builders.
5. Data security and compliance. You can design an AI agent to comply with industry-specific regulations like HIPAA. Also, since the AI agent is built specifically for your company, it will support your company’s security policies and ensure your customer and employee data privacy.

How to Build an AI Agent: Essential Steps

Let’s discuss how to build custom AI agents step-by-step.

Step 1. Define what tasks your AI agent will perform

Identify the problem your AI agent will be solving. Do you need a product recommendation system? A game-playing assistant? A custom voice AI agent? Or sophisticated AI-powered software for a self-driven car? 

The most common approach businesses use for identifying tasks for a modern AI agent is looking at the repetitive tasks performed by employees. That is usually unique to each business process, yet can be fully automated with modern AI agents. That may include preparing marketing materials, social posts, presentations, generating custom visual reports and XLS tables, and much more.

Understanding the purpose of your AI agent will help you decide how it will perceive the environment and what actions it will be taught to take. Here are examples of questions you need to answer when planning your AI agent:

• Why is the AI agent being created? 
• Is the AI agent reactive (e.g. responding to the environment like a thermostat) or deliberative (planning ahead like a navigating robot)?
• Will it interact with digital or physical environments or both?
• What kind of input will the AI agent receive — sensory, textual, visual?
• What kind of actions will the AI agent take — pre-defined, based on the model learned, or self-modified?
• How will your AI agent improve its decision-making — e.g. through supervised learning or reinforced learning?
• What computation, storage, and memory resources are required to power your AI agent?
• If the AI agent is expected to collect sensitive data, how will it be handled?
• How will your AI agent scale if it needs to handle a larger number of requests or a higher data throughput?

Step 2. Build an AI agent development team

Your software developers should have rich knowledge of various AI-related fields depending on the features of your AI agent. For example, you don't need computer vision experts for a text recognition-based AI agent.

However, to cover the core AI agent capabilities, your AI developers need to be proficient in the following niche tools and mechanisms: 

NLP, or natural language processing — includes everything from text analysis to speech recognition and enables machines to understand, interpret, and generate human language.

ML, or machine learning — it enables AI agents to learn from data to identify patterns and make predictions. Machine learning can be of three types: supervised learning, unsupervised learning, and reinforcement learning.

LLMs, or large language models — these are deep neural networks trained on vast datasets to perform tasks that involve natural language understanding and generating.

RAG, or retrieval-augmented generation — RAG combines LLMs with information retrieval techniques to improve a model’s responses. The model doesn’t rely solely on the ‘knowledge’ it already has but also collects relevant data from external sources such as databases or documents.

SFT, or supervised fine-tuning is a technique used to adapt pre-trained LLMs to specific downstream tasks, i.e. tasks that depend on the output of a previous task or process, using labeled data.

This is an incomplete list of tools required for developing AI-powered systems. A more detailed example of the technology stack needed for building an AI agent is provided further in this article.

Step 3. Define what training data is needed

You’re likely building a domain-specific AI agent. Let’s assume, it’s a custom AI agent you want to employ for code reviewing. Instead of training it on the general information from the internet, you can hand-pick only what is relevant for your particular use case, i.e. code and code auto-tests. 

Step 4. Collect the data

Building a custom AI agent requires selecting relevant data sources for training. For example, to train a code review AI agent, your development team will need to collect publicly available code from code repository platforms like GitHub.

Most likely, your development team will have a group of people to collect such a dataset. They will literally copy and paste hundreds of thousands of lines of code and organize them in a way that the machine learning model can process it. 

In other words, they will collect a narrow set of data and feed it into an existing machine learning model to get very specific results — custom code relevant to a particular task, e.g. generating a unit test automatically. 

Step 5. Choose the model

After the advent of ChatGPT, the GenAI market has been experiencing a boom that is likely to continue for the foreseeable future. Developers have in their disposal more and more generative AI models on top of which they can build custom AI solutions. What is especially encouraging is that there are increasingly more open-source models.

Some generative AI models are presented in the table below.

If you don’t have an AI engineering background, it can be hard for you to decide which model to use for your project. No worries, your development team will help you make the right choice. 

Professional AI agent companies can even conduct specific tests to figure out which models are best suited for your purposes, including from the associated costs perspective.

Step 6. Train the AI agent

Training an AI agent is essentially teaching it to respond to a set of conditions. In the case of GenAI, it understands natural language and responds to users in a human-like manner, i.e. following the line of the conversation and within a given context. 

Your development team will be using ML, which I’ve mentioned earlier, to train your AI agent. They will need to create an ML environment, i.e. install ML libraries and frameworks, load the training dataset into this environment and set the training process parameters. 

The training process consists of multiple rounds. During these rounds, the AI agent adjusts its internal parameters to minimize errors. The development team will be monitoring how the AI agent is learning and adjust the training parameters, if needed. 

This is a high-level explanation of AI agent training. I will spare you the details as most likely it’s your AI development team who will be handling the process.

Step 7. Test and validate the AI agent

After you've developed your AI agent, one of the key steps is testing and validating it to make sure it works as expected and meets the goals you've set. This process involves spotting and fixing any issues before you roll out the AI agent in full production.

The testing phase usually includes different types of testing. Each of them focuses on specific aspects of AI agent operation. 

• Functionality testing is checking whether the AI agent meets functional requirements stipulated in the technical specifications.
• Performance testing is evaluating the AI agent’s responsiveness and stability under a workload. 
• Usability testing is observing how real users interact with the AI agent versus how they are expected to and understanding what can be done to improve user experience. 
• Security testing is evaluating the AI agent to make sure it’s secure and protected from vulnerabilities.

Additionally, you need to run tests that focus on AI agent workflow and check the following:

• if the AI agent follows instructions,
• if the AI agent takes steps in the correct order,
• if the AI agent helps the user to achieve the ultimate goal,
• if the AI agent leads the conversation effectively,
• how the AI agent handles inputs with errors or incomplete data — also known as edge testing,
• how it handles sensitive information, etc.

The last step is to integrate your AI agent with your existing systems and monitor its performance. 

If your AI agent performs up to expectations — congratulations! You’ve just become an early adopter of this revolutionary technology and will make it big.

Technology Stack for Building an AI Agent

Building an autonomous enterprise-grade AI agent is a complex endeavor. It involves a wide array of technologies to implement tasks like data processing, machine learning, real-time decision making, integration with existing software systems, ensuring security, etc.

Below is a list of tools AI agent developers will need to build such a solution. Professional AI engineers will explain which tools they use and for what purposes. So, this list of AI tools is only for your reference to help you hire AI developers with proven experience. 

1. Key Technologies for Data Management

These are needed for collecting, cleaning, and processing data that an AI agent will use for learning and decision-making.

• Relational databases: PostgreSQL, MySQL, Oracle, SQL Server
• NoSQL databases: MongoDB, Apache Cassandra, Amazon DynamoDB
• Vector databases: Pinecone, Faiss, Qdrant
• Data storage: Amazon Redshift, Google BigQuery, Snowflake, Apache Hadoop, Apache Hive
• Data processing: Apache Kafka, Apache Spark, Pandas, Dask, Scikit-learn, Pytorch DataLoader

2. AI and ML Frameworks

These include machine learning, reinforcement learning, and deep learning models.

• Frameworks for developing LLM-powered applications: LangChain, LangGraph, LlamaIndex
• Machine learning frameworks: Tensor Flow, PyTorch
• Custom training: Kubeflow, MLflow
• Reinforcement learning: OpenAI Gym

3. Natural Language Processing 

NLP helps AI agents understand and respond to human language.

• Text processing: spaCy, TextBlob
• Conversational AI platforms: Rasa, DialogFlow

4. Real-Time Data Processing and Interacting

AI developers use these tools to enable AI agents to process and react to real-time data, providing timely responses and taking action.

• Real-Time Data Processing: Apache Kafka, Apache Flink, Web Sockets
• Edge Computing: NVIDIA Jetson, AWS IoT, LiteRT

5. Key Technologies for Cloud Management and Orchestration

Developers use these tools to ensure the scalability of AI solutions.

• Cloud Providers: AWS, Google Cloud, Microsoft Azure
• Containerization and Orchestration: Docker, Kubernetes
• CI/CD: GitLab CI/CD, Jenkins

6. Key Technologies for Security and Compliance

To ensure that an AI agent is compliant with regulations like HIPAA or GDPR, developers should use the following:

• Data Security: encryption techniques like TLS/SSL, AES, and RSA
• Privacy and Compliance: secure ML tools like PySyft

7. UI/UX Frameworks

Along with the niche techs, you’ll need conventional frameworks for implementing your AI agent’s user interface 

• Web interface: React, Vue.js, Angular
• Mobile interface: React Native, Flutter

Real-Life AI Agent Examples

Now that you’ve learned the bare basics of AI agent development, let’s take a look at some of the real-world examples of AI agents.

1. Salesforce Einstein

Salesforce Einstein is a set of AI technologies for CRM. It’s a ready-made solution that requires no data preparation or model management. It’s part of the Salesforce platform. To use it, you only need to put your organization’s data in the Salesforce system.

2. ServiceNow Virtual Agent

ServiceNow Virtual Agent is an AI-driven tool for IT service management and employee support. It has features like AI chatbots for incident resolution, request management, etc., natural language understanding that enables it to interpret and respond to employee queries, etc.  

3. Zendesk Answer Bot 

Zendesk Answer Bot is a customer service AI agent that helps maintain customer support and resolve tickets without humans getting involved. It generates automated responses to customer queries using ML to understand natural-language questions.

4. Asserta

Asserta.ai is an AI coding assistant designed specifically for software developers. It’s trained on unique data collected exclusively for automated context-based unit test generation. The beta version is free and supports all popular programming languages, including Java, JavaScript, Kotlin, PHP, Python, Swift, and TypeScript. It also supports multiple IDEs — JetBrains and VS Code are among them.

How DevTeam.Space Can Help You Build an AI Agent

We've answered the question ‘What is an agent in AI?’, discussed the advantages of agentic AI for business, talked about the advantages of crafting a custom AI agent versus purchasing a ready-made solution at an AI agent marketplace, and explored the process of custom AI agent development. 

If you wish to make a highly customized AI agent with unique features that suit your specific business needs, you’ll likely need to partner with a company that provides professional AI development services.

DevTeam.Space is a custom AI development company that offers expert AI developers with experience in implementing top-class AI applications. Some of the AI-powered projects we implemented include an AI coding assistant, an AI app for trying on hair color virtually, and a nature protection market intelligence platform for organizations that have climate targets to meet.

Our AI developers are proficient in implementing AI agents architecture, design, development, testing, and deployment using all major AI development tools. These include programming languages like Python and R, ML frameworks such as TensorFlow and PyTorch, specific libraries such as Keras and Scikit-Learn, and all other niche technologies. 

At DevTeam.Space, AI developers have vast experience in machine learning model development, fine-tuning, and evaluation. We work with the most advanced LLMs like ChatGPT, Llama, Claude, Gemini, etc. We assist our customers with AI applications’ quality assurance, performance optimization, and security audits. AI software development teams at DevTeam.Space have helped companies across multiple industries build market-competitive custom AI agents. 

You can let us know your initial AI agent specifications via this form, and one of our tech account managers who supervised AI agent development projects before will contact you at the earliest to discuss the details.

Frequently Asked Questions on How to Create an AI Agent

How Much Does It Cost to Build an AI Agent?

The cost of building an AI agent that is trained on general data and can perform one or two simple actions ranges between $50,000 and $200,000, depending on what particular data is used and what automated actions the AI agent will perform. 

More complex AI agents trained on a custom dataset that are able to analyze data and take multiple consequential actions unique for your business processes are more expensive to develop. The development cost starts from $200,000.

How Can I Create My Own AI Agent?

You can use out-of-the-box tools available on the market to create an AI agent. However, you need at least basic understanding of AI and how it works. If you or your team lack this expertise, you can partner with an AI development company that will assist you in building an AI agent.

How Long Does It Take to Build an AI Agent? 

Simple AI agents like chatbots will take 1 to 3 months to complete. Medium-complexity AI agents like recommendation systems take 3 to 6 months to develop. Timeframes for building complex AI agents, e.g. enterprise-grade systems with predictive analytics and decision-making functionalities are 6 to 12 months and even longer.

Sophisticated AI agents involve using advanced technologies such as machine learning, reinforcement learning, deep learning, etc. One of the longest parts of work is compiling a customized training dataset for an AI agent that’ll serve a specific business sector.

How to Build AI Agents from Scratch?

Developing a customized AI agent is complex and involves assistance from a professional software development team, including niche experts such as AI/ML developers. To build a custom AI agent, you’ll need to undertake the following steps: define the project and environment, gather an AI development team, collect and clean data for the training dataset, train the AI model, select a tech stack, design, develop, test, and launch the AI agent. You can consult DevTeam.Space on your needs.