Our end-to-end AI agent development services are not limited to writing code. AI agent development company Belitsoft helps you discover the right use cases, simple agents for quick wins and agentic platforms when you need full automation and autonomy, prepare data, design reliable architectures, implement and train AI agents, validate and test them, deploy them into the software systems you already have, and optimize performance. Belitsoft's AI agent strategy consulting makes sure that AI automation aligns with your business goals. Our integration services take care of the technical complexity of connecting agents to your enterprise infrastructure. Belitsoft's continuous improvement processes keep your AI agents accurate, secure, and cost-effective. Our flexible engagement models allow you to implement AI agents at your own pace. Hire Belitsoft, an experienced developer of AI agents, to increase revenue and trim costs, resulting in higher profits for your company.
Benefits of AI Agents
Getting more done with less effort
ROI benchmarks from different industries show that companies using AI agents see 20–40% productivity improvements and 30–60% fewer errors in repetitive processes.
Cost reduction and ROI
AI agents automate routine tasks to cut labor, training, and operational costs by 15–35%.They allow your team to focus on nuanced situations, lowering staffing needs. Professional AI agent implementation may deliver payback in 6 to 18 months.
Personalized customer experiences
Generative AI allows agents to deliver tailored responses.AI agents can analyze interaction data to offer personalized recommendations and identify negative customer trends in advance.
Better decisions
Predictive AI agents can process large volumes of structured and unstructured data to generate forecasts and recommendations. They analyze large datasets to find patterns that help businesses decide where to spend their money.
End‑to‑End AI Agent Consulting, Development and Integration Services
Where to Start AI Agent Development
We set up a discovery call within a day after you reach out through a contact form, email, or phone. You tell us your goals, what you need to do, how much you're willing to spend, and your timeframe.
We sign an NDA to protect your secret projects. After a few days, you get a detailed proposal with everything: what you'll get, when, and what it costs. If you agree to the plan, we put together a team and begin working on the project. To help you implement AI faster, we provide templates for figuring out where to apply AI, security and compliance checklists, tools to evaluate how ready your data and team are for AI, data assessments, and a step-by-step plan for implementation. These help you plan investments wisely.
How to Choose Cooperation Model with AI Agent Development Company
As an AI agent developer, Belitsoft has flexible cooperation models to fit your needs and budget. You can hire a dedicated team of AI agent developers who work just for you. You can extend your existing team by adding our AI Agent developers temporarily. Or you can hire us for a project with a fixed scope and timeline. We help you scale up or down quickly and hire only the expertise you need. We can also provide ongoing support to improve and update your AI agents after they go live.
AI Agent Consulting
Before you ask us to build an AI agent, we help you determine whether an AI project will pay off. We talk to your staff and review the processes you want to automate to understand where you can save time and money. We evaluate potential automations based on how much value they could deliver compared to how long they take to implement. That way, your first projects have the best chance to succeed and show positive ROI quickly.
We help you define what AI success looks like before you invest: how much time you save, how much cheaper the process becomes, and how much the results improve. We recommend what kind of AI system you need and which models to use based on your business goals, not what is popular right now. We start with a small pilot to measure results and provide proof.
Data Preparation for AI Agents
Most business decision makers, 91%, see good company data as the foundation for successful AI implementation. However, up to 90% of enterprise company data is unstructured, and it is difficult for AI agents to find the right information, so they give unreliable answers. McKinsey reports that 70% companies experience data challenges in GenAI implementation.
Belitsoft helps companies prepare their data and implement retrieval augmented generation that connects AI with relevant knowledge.
AI Agent Architecture Building
Before our AI agent engineers write any code, they decide what underlying software your agent will use: the bigger and smaller language models (LLMs and SLMs), tools to organize how everything works together (like LangChain or CrewAI), where to store the data (vector stores), and how the AI agent stores and retrieves information during conversations.
They also figure out how much it will finally cost to run the AI agent (model usage fees, hosting, and storage), how to make sure the AI agent doesn't do anything unsafe, and what tests need to be written to see if the AI agent works.
AI Agent Engineering
We take the LLM and turn it into an AI agent that does exactly what your company needs. Our engineers write custom system prompts that tell the LLM how to respond. They connect it to your company's other business systems and data so it can answer questions accurately about your business. Our engineers implement controls on what your AI agent can see and edit. They also add memory so it remembers what you talked about the last time you used it, so you do not have to teach it your business every time. If your business operates in a specialized field like healthcare or finance, they can train your AI agent on those types of documents so it performs at the level you need.
AI Agent Integration
Most companies have disconnected systems: knowledge bases, ticketing systems, CRMs, ERPs. That makes it hard for AI agents to do anything useful because they can't access the data they need or take actions in the right place to demonstrate effect.
We connect your AI agent to platforms and core business systems your team uses every day.
We also break your business functions into small containers that your AI can plug into. That means, for example, when a customer asks a question through chat, your AI agent can automatically check their order details stored in the CRM, create a support ticket if needed, and add a task in your ERP system automatically.
Without these connections, your AI agent stays a demo.
We also help you choose the right platform which fits your systems. Amazon Q, Copilot Studio, CrewAI - each has its own connectors for CRM, ERP, support tickets, and messaging tools. The more supported applications a platform has out of the box, the faster you get from prototype to something your team uses.
AI Agent Testing
AI agents fail more often than regular software. LLMs behind them don’t always give the same answer to the same question, and the way a question is asked can change the response. You need extra testing to catch these problems.
AI Agent Evaluation Testing
We test every tool your agent calls, every API it connects to, every database it queries, and every decision it makes while doing real work for your business. We make sure it completes business tasks correctly from start to finish. If your application has multiple steps, we check each of them.
AI Agent Performance Testing
We put your AI agents under heavy load to simulate thousands of users using your system at the same time to see how it performs. We verify that response times stay short and that your AI agent does not crash.
AI Agent Penetration Testing
We simulate attacks (prompt injection, etc.), on your AI agents the way hackers would to test how they respond under those conditions. If reports and documentation show vulnerabilities or potential exploitation, we develop and implement fixes.
AI Agent Deployment
We deploy enterprise AI agents end to end.That means we help you choose secure hosting for your AI agent to run, either on your servers or in the cloud.It connects to any databases and sources you want the agent to use.We also set up oversight so you can monitor what you agent is doing.
MCP AI Agents
The Model Context Protocol allows your AI to change records in your ERP, CRM, and other systems. It can also start workflows, get data from within your company, and connect several steps to complete complex tasks.
Multi-LLM AI Agents
You decide where to run your AI agent. If you need recommendations, we compare hosting services like OpenAI, Anthropic, AWS Bedrock, Azure, your own servers, or a mix of these options. We review costs, response times, data storage locations, and the effort required for setup and suggest what best fits you.
Security Gateway for AI Agent Calls
We use a gateway architecture where every interaction your agent has with any system passes through a controlled point. This gateway routes requests to the right model, reduces costs based on prompt caching, and logs every request and response.
AI Agent Platforms with Guardrail Features
Before each request reaches your AI agent, it's checked for potential issues. If it violates your policies or looks suspicious, it's blocked. We also validate proposed agent actions by checking them against your business rules in real time. This prevents the risk of unauthorized changes to your files and data leaks from your system.
AI Agent Optimization
AI agents need constant monitoring to understand where they make mistakes, where their decision making needs adjustment, and where customers are not satisfied based on user feedback.
Our team improves the AI agent by rewriting its instructions, updating its factual knowledge, and adjusting model settings. Reinforcement learning can be used so the AI agent learns from its mistakes.
We can cache similar prompts so the AI responds faster by skipping repeated computation, which saves time and money. Our engineers can also retrain the model behind the AI agent to keep it aligned with recent policies and products.
Custom AI Agent Development Cost
Building an AI agent is similar to building a new software product. The price depends on how ambitious you want to be.
A small company can launch a simple AI agent for a few thousand dollars. A large enterprise building autonomous multi-agent systems, where several AI components coordinate tasks together, usually plans for budgets in the tens of thousands of dollars.
What Affects the AI Agent Development Cost
Total cost depends on how complex the agentic AI system is, how many other software programs it connects to, how much data preparation is required, which compliance standards apply, how experienced the specialists must be, and whether ongoing support is included.
The more independent components you add, the more work it takes to make them work together correctly. You need specialists who understand mathematics and coding, know how to work with your company data, and can make AI agents function as expected. You also need professional testing using different scenarios.
Data cleaning and formatting often consume additional budget. Building retrieval pipelines with RAG also adds infrastructure costs.
Connecting an agent to CRMs, ERPs, payment systems, or multiple APIs requires custom connectors and can add cost per integration. Focusing on business results rather than multiple consultants’ estimates makes it easier to start small.
Hiring AI engineers, data scientists, prompt engineers, DevOps specialists, and QA specialists adds cost. More specialists and longer timelines increase budgets. Hourly rates vary widely. North American engineers often charge $120–$200 per hour, while rates in Eastern Europe start around $40 per hour.
If you operate in regulated industries, compliance specialists are needed to avoid late-stage redesigns that add time and expense.
After launch, expect to spend on LLM API usage and cloud hosting each month.
AI Agent Development Pricing by Agent Type
Basic and intermediate custom AI agents for business
Building chat assistants or workflow helpers with moderate complexity that use existing LLMs and a few integrations typically starts around US$10,000, depending on scope. The work may require dedicated backend development, user interfaces, and ongoing maintenance. If you add more connections or a custom interface, the price increases.
AI agents for specific industries
Agents that rely on trained models to perform defined tasks for your industry may start around US$20,000. They require training, data preparation, and more complex integration work.
Enterprise AI agent systems with complex decision flows and multiple integrations are priced higher. Fully autonomous agents that plan, reason, and take actions may start around US$30,000.
Multi-agent AI systems
Solutions where several AI agents work together may start around US$50,000. The added coordination logic and supporting infrastructure increase the budget.
How To Build AI Agent
Focusing on business results rather than multiple consultants’ estimates makes it easier to start small.
Belitsoft usually advises clients to start with a narrowly scoped prototype to prove the idea without a large budget. Instead of building something from scratch, use existing AI models and connect only the key programs required for one use case. Integrate only the systems necessary for the initial return on investment.
If the prototype works and delivers valuable results, you will have evidence to justify further development. Open source libraries can reduce upfront costs.
You see benefits early and can then grow step-by-step.
AI Agent Development Process
Assessment Phase
What you think you want from an AI agent and what will actually solve your problem may be different. For example, a solution that works somewhere may not fit your context (constraints of your existing systems and processes), or you may overestimate or underestimate what is possible.
In the assessment phase, we validate or challenge your hypothesis, uncover hidden blocks you did not consider, find quicker wins you may miss, and prevent building the right solution to the wrong problem.
Scope Phase
We spend time with you understanding how the business process you want to automate with an AI agent works now. We need to know every step, including where the data comes from and where it goes. Think of it like drawing a flowchart of the daily tasks of a staff member whose work you want to automate.
We write down what your staff will stop doing. Which daily tasks will be handled automatically? Which ones does a person need to review before they are finished? When does a human need to step in to help?
We also agree with you on the constraints, the rules your AI agent cannot break. For example, it cannot send emails without approval or it has to work with your current CRM system. We also define the KPIs you expect after AI agent automation, for example cutting response times in half or something similar.
Architecture Phase
At this stage, our engineers design how the AI agent will work technically. Will it react immediately to each event, or will it create a detailed plan first and then execute it? You do not need to worry about the technical details. We recommend what fits your situation best.
They map out which systems connect to each other, where information is stored, whether on your servers or in the cloud, how it moves between systems, and who controls access to it.
They decide which AI tools to use, such as ChatGPT, Claude, or others, and how to connect them to your existing software.
They also plan risk mitigation by asking what could go wrong and preparing backup plans. If the AI makes a mistake or goes down, how do we catch errors?
AI agents are not free to run - they use computing power every time they work. At this stage, you calculate what your monthly bill for using an LLM behind AI Agent will look like.
Prototype Phase
We create a working demo version of the AI agent. It may not be the final product, but it is functional enough to test because it must work with your company’s real documents or orders from your CRM.
At this stage, you can see how many out of 100 tasks are completed correctly and without hallucinations, such as citing a company policy you do not have.
You also get information about the operating costs, how much it will cost to run at full scale.
This helps you decide if the AI agent is good enough or if you need to improve it before launch.
Development Phase
Once the prototype works, developers create the working version of the AI agent - a digital worker you can count on.
Engineers write instructions for the AI and see what works. They test the prompts and improve them. It is like training a new employee. You watch them work, tell them what you want, and adjust your training.
They figure out how much information the AI needs. Too little information and the AI gives unhelpful answers. Too much and it gets slow, expensive, and confused by irrelevant details.
They build all the necessary connections to your business systems, platforms, databases, and so on. Each connection has to be secure.
Launch Phase
The testing process starts by giving the AI only 5% of the business tasks to see if it works well. If it does, after a few days we increase the percentage: first to 10%, then 25%, and finally 50%.
We set up the AI to run on multiple servers in different locations. If one server crashes, the others instantly pick up, so your customers never know anything happened.
On their monitoring screens, our engineers watch requests per minute, response times, error rates, and costs. They set up automatic alarms. If the AI starts making mistakes at an unusual rate, response times suddenly triple, or costs spike unexpectedly, the system sends a warning.
When the AI agent proves reliable, they give it more tasks. They scale it up until it processes everything it was designed for.
Time Required To Develop AI Agent
Proof of concept, or prototype, is typically delivered in 2 to 4 weeks.
Pilot implementations last 2-3 months. If the prototype works as expected, the pilot program will include additional use cases, some API integrations, basic retraining of the model, and more testing before full deployment.
Full deployment of a multi-agent AI system takes between 6 months and a year. You need to set up monitoring that meets enterprise standards and roll out the AI agent software across all your business processes. If multiple AI agents must work together or if the solution must comply with strict regulations, it could take longer.
Difference between AI Agent and Chatbot
Agents do things rather than just say things.
Traditional AI tools, such as chatbots, use an LLM only to execute cognitive tasks such as reasoning and generating text. They suggest what you should do yourself.
AI agents execute operational tasks, such as modifying systems, triggering workflows, and taking real-world actions. They can perform tasks for you because they are capable of moving beyond just suggesting what to do.
An AI agent takes a task as a goal, breaks that goal into sequential subgoals, calls the right tools or APIs, executes actions, and adjusts its actions based on the intermediate results it receives during the process of performing the task.
AI agents also use an LLM, which tells them what to do. They execute the task, and then the LLM checks whether it has been completed before presenting you with the final result you expect.
Difference between AI Agent and Agentic AI
AI agents are software programs made for one specific goal, or just one job. It may be a business process such as fraud detection or invoice processing, whatever.
An AI agent gets the job done in this domain without you telling it every step of how to do it.
For example, a refund AI agent checks orders, sees when the customer bought something, checks whether the item can be returned according to your store policy, calculates how much money to refund, instructs the payment system to issue the refund, and emails the customer with the details.
The architectural design of AI agents is so focused that they are fast and reliable for this particular goal.
The difference between an AI agent and Agentic AI is the difference between a single-agent system, focused on one workflow, and a multi-agent or orchestrated agentic system.
Agentic AI combines specialized agents, planning and memory modules, tool integration, and retrieval-augmented generation.
Agentic AI takes a big objective, such as managing all customer support, and figures out how to get it done. It breaks big jobs into smaller ones and decides which AI agents and other tools process each part. Then it makes sure that each job is done in the right order.
If a customer emails asking for a refund, wants to exchange the item for a different product, and has a question about loyalty points, an agentic AI system interacts with several agents and other programs.
- The refund agent processes the refund.
- The inventory agent reserves a replacement item.
- The shipping agent receives a command to send the new item.
- The loyalty agent updates the customer’s points balance.
- Finally, the communication agent sends one email to the customer explaining that the item was returned, a new one is on the way, the points have been adjusted, and the new item should arrive soon.
If something breaks, such as a new item being out of stock, an agentic AI system can make corrections to the initial plan and automatically search for similar alternatives.
The agentic AI system keeps track of everything during a transaction. It makes sure each agent is doing its part and that no information gets lost along the way.
The architecture of agentic AI is designed to manage complex situations and adjust when they change. It can work through interconnected problems intelligently, maintaining focus on the overall goal until it is achieved.
How Does an AI Agent Work
An agentic AI platform uses its planning module and LLM brain to break goals into concrete steps and determine the best order to execute them. Using API connectors, it can run code, update databases, and perform other actions. These are capabilities that generative models alone do not have.
By storing findings in short-term and long-term memory, properly engineered AI agents can recall past successes or failures to improve over time. There may be a reflection component in which the agent evaluates whether its actions are moving it closer to the goal, which helps it correct its course when things go off track.
An AI agent repeats a decision-action cycle, planning, executing, reviewing results, and adjusting, until the objective is achieved.
What Is a RAG AI Agent
A RAG AI agent makes sure that the answers of your AI agent are accurate, compliant, and up to date.
LLM models can hallucinate or generate answers that look convincing but are false. By default, they use static training data, so they do not know what has happened since they were last trained.
RAG fixes this by allowing the LLM to retrieve information from your company's actual files and databases.
When someone asks a question, the RAG AI agent retrieves the relevant documents and data, then feeds that context to the LLM along with the question. Instead of guessing based on what it learned during training, it answers using your current, specific information.
