AI Agent Development: How to Build an AI Agent + Cost in 2025

AI is no longer a futuristic concept. By now, almost 800 million people use ChatGPT and Gemini for quick answers, brainstorming, and problem-solving. These GenAI models are undeniably powerful but they lack context and autonomy.

Ask ChatGPT to check your flight time, and it won’t have an answer unless you fill in the details. AI agents have filled this gap, which has increased its demand in the market. If you are wondering how to build an AI agent, you are on the right track.

So, what is an AI agent?

An AI agent is a software that can plan, take action, and even upgrade on its own from previous experiences. AI agents are excellent assets for your business if you run an online store that requires customer service or if you are a SaaS provider who is having difficulty in organising client meetings and follow-ups.

Here’s how creating an AI agent might be beneficial:

• Easily manages high-volume and repeated tasks so you can focus on what is more important.
• Reduces operational costs by nearly 35% and training new staff gets cheaper.
• AI agents remember the context and provide customized experiences to each customer.
• Stay ahead of your rivals by delivering quick and customized responses relentlessly.
• Transform workflow data logs into actionable insights to make smarter decisions.

So, now it’s clear that AI agents are helpful for any business. How do you create one? How much does it cost for AI agent development? If these questions are bombarding your mind, this blog will answer all your questions. Read on!

Key Takeaways

• AI agents are autonomous software systems that act independently to solve business problems.
• Choosing the right type of AI agent is important to achieve your business objectives.
• Building an AI agent is a gradual process from setting clear-cut goals to maintaining it properly.
• The cost of an agent varies greatly depending on its complexity and capabilities.

Choosing the right Agentic ai development company will help you to create a scalable and cost-effective AI agent.

Types Of AI Agents

AI agents are classified according to their level of intelligence, decision-making abilities, and how they interact with their surroundings.

Companies that are looking to learn how to create an AI agent must know about the types of AI agents. So that they can choose the right one based on their technical complexity and costs. We have listed the main types of AI agents that have their own strengths and applications.

👉 Simple Reflex Agents

This is the most basic type of AI agent. A simple reflex agent gives a direct response based on the current input without thinking much about the past experiences or future complications. Their behaviour is influenced by predetermined condition-action rules that specify how to respond to certain inputs.

Though their complexity is limited, this method makes them practical and easy to design especially in situations where there are only a few options to consider.

Example: An automated sprinkler system is a simple reflex agent that turns on automatically when smoke is detected.

👉 Model-based Reflex Agents

Model-based reflex agents are the advanced version of simple reflex agents. These models still rely on condition-action rules to make decisions but they also create their own internal model.

They are more flexible than simpler agents because they are capable of dealing with situations where the context must be retained and applied to decisions in the future. These agents are appropriate for environments in which the present condition cannot be fully observed with just data sensors.

Example: Smart home security systems use internal models of regular household activity patterns to differentiate between ordinary activities and potential security threats.

👉 Goal-based Agents

Goal-based agents use an active and goal-based approach to solve a problem. They are created to achieve certain goals while considering the long-term effects of their activities.

Unlike reflex agents that operate based on rules or world models, goal-based agents plan sequences of behaviors to achieve desired results. They apply search and planning algorithms to identify activity sequences that lead to their expected outcomes.

Example: The self-driving cars which have the destination as its goal and make decisions all through the journey to reach the goal as safely and efficiently as possible.

👉 Utility-based Agents

A utility-based agent is capable of making decisions by evaluating every outturn for its actions and choosing the one that amplifies total utility. This lets businesses to make better decisions especially when there are multiple options to choose from.

These agents perform effectively in complex and dynamic scenarios where simple decisions based on objectives might not be sufficient. They aid in balancing multiple objectives and adjusting to changing circumstances, which leads to more flexible behavior.

Example: Traffic management systems that analyze multiple factors like traffic data, accident reports, road blocks, etc and adjust signals accordingly, ensuring a smooth travel.

👉 Learning Agents

Learning agents improve its performance by understanding its environment and learn from its experiences. Other AI agents depend mostly on existing protocols, whereas learning agents continuously adapt their behavior based on user feedback. This helps businesses to function better in situations that are dynamic and surprising.

Learning agents are effective in situations where the right path of action is uncertain and must be found with experience.

Example: Streaming platforms like Netflix discover your interests and give content recommendations for a better experience.

👉 Multi-agent Systems

A multi-agent system consists of several autonomous agents interacting in a shared environment. It acts independently or collaboratively to achieve individual or collective objectives.

These agents are made to solve complicated issues in real-world situations by breaking them into smaller and more manageable subtasks. Higher-level agents prioritize extensive goals while lower-level agents perform more specific jobs.

Example: Warehouse management software that includes multiple robots working together to move and sort products.

How To Build An AI Agent

Developing an AI agent requires ample research and planning to make sure that the AI agent meets the specific business goals. It needs a well-structured approach more than simply integrating the large language models. Below, we have discussed the steps involved in creating a successful AI agent. So, continue reading to know.

1️⃣ Define The Purpose And Environment

First, make sure to identify the exact environment in which you want to place your AI agent. Do you wish to incorporate the AI agent into an app, website, or other system? This will help in clearing the way for compatibility after the implementation stages.

Once the environment has been specified, determine the tasks and functionalities that the AI agent must perform. This will vary depending on the field or industry.

Then, ascertain the responsibilities of the AI agent. List the tasks or issues you would like the AI agent to resolve. Do you need it to respond to consumer queries, assist users with online shopping, or give business information? Your AI agent’s functions should be in line with the needs it is designed to meet.

2️⃣ Choose The Right Architecture

The next stage is to choose an architecture which is the blueprint that defines how your agent analyzes information and takes action. The architecture you choose is typically determined by the type of AI agent you wish to create.

Nowadays, the majority of enterprise-level AI agents use:

• Rule-based architecture to perform predictable tasks such as classification and routing.
• Goal-based architecture is for large-scale automation and decision-making.
• Learning-Based Architecture to create adaptive platforms that improve continuously with data.

This choice is important because the right architecture ensures your AI agent is functional and cost-effective. Since every business has unique work functions, partnering with an AI agent development company can help you create an AI agent for your business.

3️⃣ Gather Data

Just like a student learns from textbooks, an AI agent learns from data. If you don’t have the right data sets, your AI agent will be invalid. The data gathered should be reliable, significant, and large. If the data is inaccurate or of low quality, your AI agent can make blunders that are unimaginable.

Data can be obtained from the following sources:

1. Internal Data: This refers to the data collected from your business operations such as sales reports, customer information, financial reports, and many other records.
2. External Data: This includes data from the public domain, commercial partners, and purchased datasets.
3. User-generated Data: This includes user data that is gleaned from social media posts, website interactions, and product reviews.

The data needs to be preprocessed after it has been collected. This covers detecting irregularities, dealing with missing data, and confirming data consistency. The process guarantees that you establish a strong and reliable basis for the deployment of your AI agent.

4️⃣ Choose Your Tech Stack

The next step is to select the appropriate programming language, framework, and libraries. It must be chosen based on how your AI agent needs to understand text, analyze visuals,  or make predictions.

Layer	Tool/Platforms	Best For
Programming languages	Python, Java, C++	Python for ML/AI libraries; Java/C++ for performance-heavy apps
AI Libraries & Frameworks	NLTK, spaCy, TensorFlow, PyTorch, OpenCV, DeepSpeech, Rasa	NLTK, spaCy for Natural Language Processing, TensorFlow, PyTorch for ML & Model Training, OpenCV for Computer Vision technology, DeepSpeech is for speech recognition, and Rasa is for web-based platforms
LLM & Agent Frameworks	LangChain, LlamaIndex, AutoGen	For building context aware AI agents
Cloud Platforms	Google Vertex AI, AWS SageMaker, Microsoft Azure AI	Vertex AI for holistic AI services, AWS SageMaker for existing AWS users
APIs & Integrations	OpenAI, Anthropic, Hugging Face	Access to 100s of pretrained models
Databases	PostgreSQL, MongoDB	Handling structured and unstructured data

5️⃣ Develop and Train The AI Agent

Now that you are ready with your essential tools and technologies, you can start building your AI agent. First, start developing the brain of the AI agent. It is the learning model with a set of rules that instruct your AI agent to act as per input in various situations.

Initially, the agent can recognize names, dates, and other elements in text. Then it aims to understand the sentence structure. After the AI agent has identified words and sentences, you must initiate conversations.

The autonomous AI agent will also begin to take the process requests and do error handling. After this, the AI system will learn to respond with appropriate answers. Now, connect your agent to the right data sources and tools.

6️⃣ Test The AI Agent

Once you have developed and trained the AI agent, give it a set of tasks to check how it reacts. Testing the AI agent is more important as you can avoid costly mistakes early. 

Testing ensures that your AI agent works as intended in real-world situations and spots any errors early on. Before deploying the agent, you can test it for accuracy using a variety of inputs and conditions.

You can perform the following testing models on your AI agent:

• Unit Testing – To examine individual components to check that it functions properly on its own.
• Performance Testing – To determine its stability and potential in responding to different situations.
• A/B Testing – Evaluate the performance of two AI agent versions to compare which one works best.

If the performance of your agent is not up to your expectations, then try adding more data or even retraining the model. Gather customer feedback through surveys or feedback forms and observe what users feel about your AI agent to make it better.

7️⃣ Deploy And Monitor The AI Agent

After your development and testing phases, you can now deploy your AI agent. Once it is set, monitor it keenly to make sure everything functions well. Track the metrics like response time, resource usage, accuracy, and error rates.

To improve your agent, you can also collect feedback. This helps you to get a clear picture of how users feel about your AI agent and opens the door to improvement.

Top 6 AI Agent Use Cases

AI agents are already shaping businesses across every industry. We have listed a few use cases to highlight how AI agents can bring more opportunities for your business.

✅ Customer Support Automation

AI agents respond to support queries through social media, email, and live chat. They can escalate complicated cases or resolve them on their own.

Example: An online retailer uses an AI agent to deliver prompt responses about their products’ shipping and tracking. Instead of waiting for human agents to respond, customers obtain real-time updates around the clock.

✅ Sales & Lead Qualification

Instead of your sales crew chasing cold leads, AI agents may qualify prospects, schedule demos, and route only high-value opportunities to employees.

Example: A software company uses an AI agent to talk with website visitors, qualify leads by asking the correct questions, and automatically schedule demos for the sales team. Thus, saving a lot of time for salespeople.

✅ Coding and DevOps Automation

AI agents help engineering teams save hours of manual work by examining the code, identifying issues, running tests, and even deploying.

Example: A software company has AI agents that can review pull requests, run tests, and even deploy changes to staging.

✅ Research & Content Generation

AI agents can make your research work easier by scanning millions of pages and giving the best results. They research and product content in seconds for your blog or social posts.

Example: An AI-powered content planner helps businesses to brainstorm new ideas, conduct research, and create first drafts based on their user preferences.

✅ Personal Productivity Assistant

An AI personal productivity assistant helps users to manage their daily tasks with ease by scheduling appointments and sending reminders.

Example: A smart calendar assistant that dynamically schedules work and personal time to improve work-life balance.

✅ Autonomous Business Operations

An advanced artificial intelligence agent that can manage everything without the need for human participation.

Example: Autonomous patient monitoring systems continuously monitor patients’ health data and alert the medical team to provide immediate treatment in case of noticing any abnormalities.​

Cost To Build AI Agents in 2025

Now that you know how to create an AI agent, let’s see the expense that comes with it. The cost might vary greatly depending on the level of complexity.

Below is the cost estimation for various types of AI agents.

AI Agent	Estimated Cost	Function
Basic AI Chatbot	$10,000 to $20,000	Handles basic customer queries.
NLP-Powered Conversational Agent	$20,000 to $30,000	Gives personalized responses by understanding the context.
Voice-enable AI Agent	$30,000 to $50,000	Combines speech recognition and NLP to converse like a human.
Process Automation Agent	$40,000 to $80,000	Connects with CRM or databases for task automation. Ideal for mid-size businesses.
AI Agent with ML Training	$1,00,000 to $2,50,000	Ideal for large organizations. This model continuously learns and handles complex tasks.

Developing an AI agent is an investment for your business. So, understanding the AI agent development cost and AI agent types is important to choose the right one that best suits your business needs.

How CONTUS Tech Can Help You With AI Agent Development?

Creating an AI agent all by yourself isn’t a cakewalk. The process can be frightening when you don’t have the right skills and resources. Yes, a few prebuilt platforms might help, but not in the long run or for complex tasks. But collaborating with AI agent development companies can reduce your burden.

CONTUS Tech is a reliable partner for businesses looking for expert guidance on how to build an AI agent in 2025. They offer advanced AI solutions that are customized for every business’s needs. The company has vast experience in the field with a record of handling more than 500+ successful projects and a high rate of customer satisfaction. 

One of the main reasons businesses choose CONTUS Tech is their flexible deployment options. You can choose either on-cloud or on-premise deployment. Their strong cloud partnerships and robust security systems make them an ideal choice for any business that looks to shine.

Looking for a customer support agent or a multi-agent system, CONTUS Tech can help you make your business goals a reality. Reach out today!