How to Build an AI Agent for Business: Architecture, Steps, and Costs (2026)

Tech giants continue pouring billions into AI development, and businesses see returns: 97% of business leaders report positive ROI from AI implementations (Forbes, March 2025). Companies can benefit from using AI in many ways, ranging from process automation to improved decision-making.

Many of these advantages can be achieved by implementing AI agents, autonomous systems powered with machine learning (ML) algorithms capable of handling workflows of varying complexities in different domains.

In this article, we explain what an LLM-powered AI agent is, how its architecture works, and how to build one step by step: from defining use cases to choosing frameworks, controlling costs, and keeping the agent reliable in production.

An AI agent is a system that uses artificial intelligence capabilities to analyze its environment, process information, and make decisions in order to accomplish specific goals. The workflows handled by AI agents can vary significantly.

For example, a customer support AI agent can handle a conversation with a company’s clients. It processes their requests and provides relevant answers based on the curated information and a deep understanding of the context. Meanwhile, a fintech AI agent can process vast loads of a company’s financial data to make meaningful projections and retrieve insights on budgeting, transactions, and profitability of the given company.

So, simply put, an AI agent is a company’s virtual AI-powered employee. Such agents are often built on top of an LLM, which is a machine learning model capable of comprehending and generating human language by analyzing massive datasets.

The difference between an AI agent and a chatbot is autonomy. A chatbot answers within a single conversation; an agent plans multi-step work, calls external tools and systems, and completes tasks with limited supervision.

Classic AI theory distinguishes simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. Modern LLM-powered business agents usually combine goal-based and learning behavior: they pursue a defined objective and improve from feedback and new data.

AI agents have a wide range of use cases. Some of the most common examples include:

• Conversational AI agents in customer support that are capable of handling basic conversations with the clients
• Analytical agents that process data to provide meaningful insights and recommendations for different domains
• Healthcare AI agents that process the data on patients and provide medical recommendations
• Fintech AI agents that analyze financial data to assist in finance planning and detect fraud
• AI real estate agents used for screening landlords, tenants, and other proptech stakeholders
• AI assistants that help software developers review and optimize code
• Tools that use ML algorithms to process logistics information to automate inventory tracking and optimize logistical routes
• AI agents for candidate screening that are applied in human resource management and recruitment
• AI virtual agents for lead management and scoring that are used in sales and marketing

Overall, these are just some common use cases for AI agents across different industries, and this list can be easily extended with other examples. The key point is that a properly configured AI agent can assist in almost any workflow that involves the processing or generation of data. Several of these workflows are implemented in the working AI agent demos built by Leobit’s R&D team.

Every LLM-powered agent, whatever it automates, is built from the same five components: a model, instructions, tools, memory, and an orchestration layer that connects them.

• Model. The reasoning engine: a commercial LLM, an open-weight model, or a custom corporate LLM.
• Instructions. The system prompt that defines the agent’s role, boundaries, tone, and escalation rules.
• Tools. Function calls the model can invoke: database queries, CRM updates, email sending, calculations. Tool access is what turns a language model into an agent.
• Memory. Conversation history plus long-term storage, typically a vector database queried through RAG.
• Orchestration. The loop that routes between model, tools, and memory, handles retries and errors, and decides when the task is done. Model Context Protocol (MCP) has become the common standard for connecting agents to data sources and tools.

An LLM serves as an AI agent’s brain, but an agentic AI system involves other tools and services responsible for completing tasks autonomously. AI agents integrate short-term and long-term memory via vector databases that store data in vectors and can efficiently detect and retrieve similarities in this data. Such AI systems use API calls and plugins for real-world interactions and apply multi-step reasoning for decision-making. When the agent retrieves company knowledge from a vector database and feeds it into the model’s context before answering, that pattern is called retrieval-augmented generation (RAG), and it is the standard way to ground an agent in business data without retraining the model.

There may also be more advanced AI agents that rely on recursive planning, a problem-solving technique that breaks down complex tasks into manageable steps that are solved in loops. Such AI systems are known as agentic AI solutions. They apply self-reflection and multi-agent collaboration for handling dynamic workflows and are capable of proactive reasoning and limited autonomy in decision-making.

While AI agents may differ in their functions and use cases dramatically, there are similar patterns in building an AI agent based on an LLM. To create such a solution, follow the steps outlined below.

Before you start developing an AI agent, you should clearly define who will use it. Will it be a customer-facing solution or an internal tool helping your employees optimize workflows? The answer to this question is crucial because it defines the entire development roadmap.

Another fundamental question is what problem your AI agent needs to solve. You may face problems with an overloaded customer support team, struggle with data analytics, or just have too many time-consuming routine processes in your workflows. Plan your AI agent to cover these problems.

Upon defining the users and the problem, come up with user scenarios and define additional business needs for the AI agent. This will help you define clear functional and non-functional requirements. Some examples of functional requirements:

• Core and additional AI agent workflows
• Languages in which it operates
• Format in which your AI agent interacts with the users (e.g., text, voice)
• Integrations with other systems and services, such as ERP, business analysis platforms, etc.

As for non-functional requirements, these may include:

• Response time of your solution
• AI agents’ request capacities and scalability demands
• Your solution’s availability and platform outreach
• Security and compliance with data protection regulations and industry-specific standards (for example, HIPAA for healthcare)
• Capabilities for monitoring, maintaining, and upgrading the solution.

Such understanding will compose a solid foundation for your AI agent development roadmap.

Before you choose an LLM for your AI agent, make sure to collect and organize the data it will use. You will need to evaluate models against your specific data to determine performance characteristics. In particular, you should understand whether an LLM can generate proper responses that align with your domain and data. It is also important to understand whether a chosen LLM can maintain context during long conversations involving your data. We have provided an overview of the LLM landscape and characteristics of different large language models in our article on Leobit’s transformation with a corporate LLM.

Possible options include current-generation commercial models such as OpenAI’s GPT-5, Google’s Gemini, and Anthropic’s Claude, as well as open-weight models like Meta’s Llama. If you are already using a custom corporate LLM, use it as a foundation for your AI agent. This will ensure a tailored approach, as your AI agent will build its workflows and decisions on curated corporate information.

Another important decision is defining your AI agent’s hosting model. For example, you may opt for a cloud-based or self-hosted on-premises solution.

If you choose cloud hosting, you can use various cloud-based services for building custom ML algorithms. For example, Azure provides a broad range of AI software development services, templates, and components to help you build an AI agent.

At this stage, you need to choose the right approach for integrating AI into your software system.

Also, you need to select a development framework and other tools to wrap up your AI agent. We at Leobit prefer using .NET as the primary framework for wrapping an LLM agent. It helps us cover the back end, while Angular is a common choice for front-end development. A fuller overview of agent frameworks and no-code platforms follows in a dedicated section below.

Most business agents do not require training a model at all. The standard approach is retrieval-augmented generation: the agent keeps company knowledge in a vector database and retrieves the relevant fragments into the model’s context at question time. RAG is faster to implement and cheaper to update than fine-tuning, and it lets the agent answer from current data.

Whichever grounding method you choose, pay much attention to data organization to ensure that your solution provides accurate and efficient outputs.

Curate the data carefully: it should be selected, evaluated, and validated by your team. For instance, you can compile a comprehensive set of documentation along with relevant content from your corporate website. Consequently, the LLM will generate responses based on this structured information.

Fine-tuning still has its place: when the agent must follow a specialized tone, format, or domain vocabulary that prompting cannot reliably produce, training the model on curated examples is worth the investment.

Hallucinations and inconsistencies are still very common challenges when it comes to using corporate LLMs. Therefore, create and implement a risk mitigation framework to address AI failures. This involves monitoring and using various techniques, such as fine-tuning and prompt-tuning, to prevent your LLM from degrading and ensure its continuous improvement.

To ensure that AI agents handle workflows that conform to user needs and scenarios, it is important to configure the AI logic. This will enable your LLM-powered AI agent to be flexible enough to manage varying scenarios, adjust to changing conditions, and provide outputs that conform to different contexts. The image below outlines the most important AI configuration workflows.

This is also the stage for guardrails: the checks that keep an autonomous system inside its mandate. In practice that means restricting which tools the agent can call and with what permissions, validating inputs and outputs against defined rules, and filtering sensitive data before it reaches the model. To keep humans in the loop, it’s also important to establish escalation paths for low-confidence or high-stakes situations.

Once you have built your AI agent, integrate it with your business applications and workflows. For example, connect it to your CRMs, ERPs, databases, financial planning solutions, or other systems, depending on your industry, toolset, and the purpose of an AI agent.

We suggest you make your AI agent available across different platforms, including web, mobile, and desktop.  A multi-channel deployment is vital for your AI agent’s usability, so feel free to use Flutter and .NET MAUI development services to ensure a unified experience across different devices. Integration also extends to data infrastructure: our AI database assistant built with MCP demonstrates how an agent can query corporate databases through a standardized protocol.

Continuous testing and optimization keep your AI agent useful and relevant over time and help you avoid degradation.

This involves:

• Monitoring performance evaluation metrics, such as accuracy, latency, and user value. In agent development these structured test suites are called evals: fixed sets of test cases the agent must pass before and after every change
• Occasionally running technical audits of your AI agent aimed at detecting performance issues, bugs, hallucinations, or security vulnerabilities
• Applying automated and manual testing strategies that help you validate an AI agent’s performance
• Ensuring continuous improvement of your AI agent through retraining cycles.

With such an approach, you can ensure reliable and efficient performance of your LLM-powered AI agent over time.

Agent tooling splits into two camps: code-first frameworks for engineering teams and no-code platforms for assembling agents from ready blocks.

Code-first frameworks give full control over orchestration, guardrails, and integrations

• LangGraph (Python/JS): graph-based orchestration for complex, stateful agent workflows
• CrewAI (Python): role-based multi-agent teams with task delegation
• Microsoft Semantic Kernel (.NET, Python, Java): Microsoft’s agent-orchestration SDK, a natural fit for .NET-centric products and Azure OpenAI deployments
• OpenAI Agents SDK: OpenAI’s framework for tool-using agents with built-in guardrail hooks

No-code and low-code platforms such as n8n, Make, Microsoft Copilot Studio, and Google’s Vertex AI Agent Builder let business teams assemble working agents from prebuilt connectors. They can actually help you build a simple AI agent without coding, and use free tiers to prototype. But there are some significant downsides platform pricing, limited control over data flow, and vendor lock-in, the same constraints covered in the integration-approach table above.

AI adoption is accelerating as artificial intelligence increasingly powers critical digital services such as analytics, automation, and data platforms. Businesses recognize that AI can provide comprehensive support across various workflows, which makes it a more promising investment than standalone investments in analytics, automation, and data platforms. In fact, the study on the rise of agentic AI suggests that AI is expected to receive more investment than analytics, data platforms, and automation.

As shown, AI stands out as the digital service with the most promising investment growth because artificial intelligence, including agentic AI, offers businesses a wide range of benefits. While these advantages vary significantly based on specific use cases, certain benefits are common across most businesses implementing AI agents, regardless of the industry.

The use of AI can bring a dramatic boost in employee productivity, enhancing it by 30%. By implementing AI agents, companies can increase their workflow capacities and automate many time- and effort-consuming routine processes. This allows human employees to focus on more complex tasks that may require human involvement and creativity. In addition, AI can assist specialists in many tasks by providing recommendations and analytical insights for different workflows.

One of the most common examples of successful agentic AI implementation for higher productivity is the use of AI agents as virtual assistants in customer support. Such systems can easily handle routine customer queries and reduce the load on human support specialists. By using natural language processing (NLP) and an LLM knowledge base, they are often capable of handling human-like conversations with customers. Agentic AI can show a deep understanding of customer intent and the ability to provide informative and relevant responses within seconds.

Not all customer requests can be handled by AI. The point is that ML capacities for understanding the context and intent, as well as researching and processing the right data are limited. Human involvement in customer support is still vital, but the ability to delegate simpler queries to AI agents can boost the overall productivity of human support agents.

AI agents can improve customer satisfaction by shortening the response time. Apart from customer support, AI agents can integrate with customer relationship management (CRM) systems to enhance some of their crucial workflows.

AI agents can handle customer segmentation, analyze client interactions to provide meaningful insights on their needs and concerns, and automate email campaigns. All such measures improve the overall quality of customer communication and boost client retention. Besides, by supporting human specialists in their workflows, AI agents enhance the overall quality of service, which is also vital for customer satisfaction.

For example, we at Leobit use Leora, an AI-powered sales assistant that provides suggestions to customer queries based on our case studies and hands-on experience in various domains. With such an AI agent, our actual and potential clients can get valuable insights on their ideas and possibilities for implementing them in the short term. Leora is not meant to handle customer requests independently. However, by assisting our sales team, it makes the user’s experience with our web platform much more convenient, enhancing positive company reputation.

AI agents are widely used for analytics in different domains, ranging from finance to marketing and advertising. Such systems can be used to automate data collection, cleaning, and real-time processing, allowing businesses to extract deeper insights faster. Whether it is about customer insights, market trends, or financial projections, the analytical capabilities of AI agents allow businesses to achieve greater precision in decision-making.

For example, in finance, an AI agent can use natural language processing to analyze earnings reports, social media sentiment, and price fluctuations to identify stock market trends. By getting precise and relevant insights, financial companies can make better decisions on purchasing and selling stock assets. This ultimately leads them to a greater return on investment (ROI).

The implementation of AI agents helps companies that specialize in selling products or services to customers enhance their sales funnel. ML-powered systems can automate lead qualification, personalize customer interactions, and optimize follow-ups. This enables businesses to move customers from acquisition to product or service delivery quicker.

For example, Leobit enhances the sales funnel with Leona, an AI-powered RFP scoring and proposal generation solution. Acting as an AI-powered virtual assistant, it automates the categorization and scoring of opportunities based on their relevance and potential value. This allows our sales team to improve targeting, find better prospects, and proceed with the best follow-up actions, increasing conversion rates.

LLM-based AI agents are often used as a source of truth for quick information checks by both customers and employees. If built on well-organized and curated data, large language models provide a consistent and relevant knowledge base on the company and its services. Such a solution will come in handy to sales specialists who communicate with customers.

AI agents can also hold great value for the company’s internal operations. It can provide employees across different departments with best practices and professional tips, guidelines, lists of corporate rules, and relevant company information.

For example, we at Leobit use Leonardo, an AI agent serving as an HR assistant. The solution responds to common questions of our employees by retrieving the relevant data from Leobit’s curated knowledge base. Leonardo reduces the workload of our HR team and ensures that our employees get the information they need as fast as possible.

We at Leobit follow the most recent AI trends in our own operations. Our team has already developed several AI employees that facilitate different workflows. For example, we are actively using Leo, an email auto-response solution for the sales team that provides fast and personalized responses to customer requests. Leo also helps our sales team categorize and score leads based on request type, industry, and technology match.

Leora, our vocalized AI sales agent, uses the company’s knowledge base to provide detailed and personalized answers to prospects.

For internal operations we use Leonardo, our AI-based HR assistant, which answers employees’ questions by retrieving the relevant information from the company’s curated knowledge base.

Finally, we have released Leona, an AI agent that automates project evaluation, helping our teams find the most suitable cooperation opportunities and automatically send project proposals to promising clients. Leona runs on Azure OpenAI for scoring and proposal generation, integrates with Google Spreadsheets for data synchronization, and is tested with Selenium WebDriver. It monitors bidding platforms around the clock and works in two modes: fully automated submission and a semi-automated mode where our SDRs review proposals before they go out.

Keragon brings agentic AI to healthcare operations. Its AI teammates handle patient intake, appointment reminders, and no-show recovery across 300+ healthcare integrations, all within a HIPAA-compliant environment.

This Japanese multinational conglomerate operates in various industries, ranging from fintech to healthcare. As a company that stands at the forefront of digitization, Hitachi uses AI agents across different domains, including manufacturing and transportation. For example, they use an AI agent for predictive maintenance of the company’s manufacturing equipment. AI agents also help Hitachi optimize train schedules, routes, and energy consumption, improving the company’s operational performance in logistics.

GitLab introduces AI agents to assist software engineers in a variety of tasks. These systems are used for internal operations but can also be delivered to development teams as open-source solutions. The company provides machine learning algorithms for DevOps, automated code reviews, and enhanced code debugging. GitLab’s agentic AI helps software development teams enhance development speed and assists them in software maintenance by detecting security vulnerabilities. We have also examined how agentic AI helps software development across the delivery lifecycle.

Adobe, one of the world’s biggest providers of visualization software, has AI agents behind its prominent tools. In particular, LLM-powered AI agents enhance essential features of Photoshop and Illustrator to automate complex design tasks and provide personalized customer insights. Finally, Adobe offers AI-powered content generation tools that have also accelerated digital media production, which drives user engagement and revenue growth.

Developing an AI agent that brings tailored value to your business is not a simple task. Mistakes may occur at any stage, ranging from planning the functionality to implementing and maintaining an LLM that powers the agent. To handle everything properly, you need a team with solid practical experience with AI and LLM technologies.

Finding such a team may be challenging, and a proven option is engaging a dedicated team of software engineering specialists.

This approach will be especially useful if you want to:

• Fill out the AI development expertise gap, as your tech team should be especially strong when it comes to complex LLM-powered AI agents
• Focus on your core business needs while delegating AI agent development to specialists
• Save time and budget required for hiring and training AI software development team in-house
• Get a maintainable solution that is ready for efficient upgrade and scaling
• Avoid common AI software development mistakes, such as poor data organization, system degradation, or poor integration into your business workflows.

With a dedicated team of experts, you get your LLM solution faster and with deep attention to the quality and functionality you need.

LLM-powered agents have moved from experiment to everyday business tooling. It can assist in a variety of workflows, which makes it a more popular investment than standalone investments in such directions as analytics, automation, and data platforms. By successfully implementing agentic AI, companies can get such benefits as enhanced customer experience, improved efficiency of the customer support team, enhanced data analytics, speed up for the sales funnel, and a consistent knowledge base.

Developing an AI agent may be a challenging task, especially for businesses that do not possess the necessary AI software development expertise. It involves many steps and workflows to handle, and a reliable way to cope with such a challenge is building your agent with an experienced AI software development partner.

Leobit, a company with a strong portfolio of successful AI software development projects and several AI-driven employees integrated into our workflows, is ready to assist. Contact us to discuss your AI agent idea and get a practical implementation plan from a team that builds and runs its own agents.