How to Build an AI Agent: Step-by-Step Guide 2025

AI agents can spell out countless opportunities to apply itself across different industries. For instance, acting as customer service agents, they can handle inquiries automatically to manage financial transactions or streamline logistics.

Being an AI development company, we’ve been building AI agents for our clients. We've shared our learnings in this article. Enjoy Reading!

To answer your question “how to build an AI agent”, let’s start with what an AI agent is.

What is an AI Agent?

AI agents are pre-programmed and trained software apps that automate tasks. These apps are built using AI technologies like machine learning models, AI chatbot development platforms, and most importantly, agent training data. Such agents may be as simple as a program that is designed to perform repetitive tasks, or as complex as a machine learning system that learns and adapts over time, through the application of machine learning algorithms.

A Capgemini survey of 1,100 executives at big enterprises found that 10% of firms already use AI agents, and 82% of them hope to incorporate them in the next three years. Around 60% answered that they intend to use AI agents within a year, while a quarter said it would take longer than that. Plus, 64 percent said the enhancements to customers’ service and satisfaction increase will outweigh the risks, 71 percent said the work will see an increase in automation with AI agents, and 57 percent said productivity gains will outweigh the risk.

AI agents are used across industries. For example:

• In customer service, AI agents can automate replies to customer queries.

• In healthcare, AI Agents can support patient management by scheduling appointments and reminding patients about drug consumption.

• AI finance agents can read through the market updates and perform trades at the best time.

How to Build an AI Agent?

The first step of developing an AI agent includes clear objectives and an understanding of the tasks it will perform.

1. Identify the Target Tasks

This step to build AI agents includes providing a detailed description of the specific responsibilities the AI agent will take charge of. It might cover anything from responding to questions on a website to suggesting something based on the user's behavior.

Here are some popular use cases of AI agents:

• Decision-Making Assistance

• Customer Support

• Data Processing & Analysis

• Workflow Automation

• Fraud Detection

• Healthcare Support

Here to be pointed out, the AI agent use case will significantly affect the AI agent architecture and the cost to develop it.

2. Gather AI Training Data

AI agents rely on high-quality, structured data to function effectively. Decide what kind of data your agent will need privileges to, like user inputs, database records, or real-time data. Ensure this data is presented and organized, so that the agent can use it appropriately.

The type of data needed depends on the agent’s purpose:

• Enterprise Data: HR Manuals, Financial Reports, Compliance Regulations.

• User Interaction Data: Chat logs, customer queries, and support tickets.

• Structured Databases: CRM records, knowledge bases, and product catalogs.

• Real-time Feeds: API integrations for live updates (e.g., stock prices, weather).

• Sensor Data: IoT and automation systems for smart AI agents.

Data Preprocessing & Cleaning

• Remove duplicates, normalize values, and filter biases from the data.

• Ensure labeled data for supervised learning.

Synthetic Data & Continuous Learning

• Use data augmentation & simulations to expand training.

• AI should learn from user feedback and update continuously.

• Reinforcement learning should also take a part in continuous learning.

Example: A financial AI agent would clean, bias-free loan approval data to prevent unfair decisions.

3. Planning AI Agent Tech Architecture

It is important to choose the right set of tools and AI platforms to make the AI agent efficient. The architecture design of AI agent will depend on the level of task complexity and the scope of work. If you don’t have an experienced team of AI engineers, you'll need to connect a development partner for this. You can search one in this list of the top 10 chatbot development companies in US.

Moving forward, if you’re planning to take care of the development process by yourself, here are some things you should consider:

A. Select the Right Large Language Model

Large Language Models (LLMs), such as GPT-4o and Llama, are revolutionizing AI agents by enabling more natural, context-aware interactions. Choosing the right LLM for your AI agent requires a strategic approach based on several factors: the complexity of the task, the volume of text data it will process, and the level of personalization needed.

For instance, GPT-4 excels at generating human-like responses, making it ideal for customer support chatbots, virtual assistants, and content generation. However, selecting the best model isn’t just about linguistic fluency—it also involves evaluating performance, scalability, and cost-effectiveness.

Additionally, consider the model’s ability to fine-tune itself to your specific use case, ensuring that it not only understands context but also adapts over time to improve accuracy and relevance.

B. Scalability and Support

Consider tools that can scale up on demand, and at the same time ensure reliable support as the AI might experience a sudden increase in user requests. This is important for AI agents that are built for a mass userbase.

C. Cost of Technology

In addition to the development efforts of developers, there are other costs to AI agent development. The costs include AI platform licensing fee, LLM token fee, and other third-party services. To minimize the cost of technology, you should consider consuling an experienced AI company. Companies like us create the best technology architecture for your AI agent use cases.

D. Modularity & Concurrency

A modular architecture for an AI agent structures its functionality into independent components—such as data processing, decision-making, and action execution—allowing each module to operate separately while communicating with others.

Moreover, if your AI agent tends to deal with multiple tasks at the same time, design it to operate concurrently. This can be done using asynchronous programming, or by implementing microservices that can work in parallel.

4. AI Agent Development Process

The AI agent development process involves coding, API integration, and testing:

A. Coding the AI Agent

Core Functions: Start with programming of basic features, for example - data management, decision-making, and user interface.

Modular Development: Design each element of the designated module, based on the modular system discussed earlier.

B. Integration with External Systems

API Connections: Integrate the AI agent to appropriate APIs, that will enable you to extract data or implement other features.

Database Integration: This step includes designing databases to store and collect useful details in the agents' operations through interactions.

C. Implementing Learning Capabilities

Machine Learning: If accessible, assimilate machine learning algorithms like TensorFlow, by using the libraries to feature the agent's ability to learn from the data.

Memory Systems: Implement memory mechanisms, using beneficial technologies to enable the agent to remember how to interact with the user, or what his preferences are.

D. Testing the AI Agent

Unit and Integration Testing: Conduct the testing of individual modules, and their interconnection parts to ensure they work as designed.

Performance Testing: Test the agent across various conditions for its stability, and readiness to react to these conditions.

E. Documentation

Code Documentation: Comment on the program to ease the process of later adjustments and corrections.

User Documentation: Draft guides and how-to videos for users that show them how to interact with the AI agent.

5. Refining AI Agents Through Iteration

Successful AI agents require continuous improvements through iterative training cycles. Best practices for iterative AI improvement include:

• Monitoring real-time user interactions and identifying weak response patterns.

• Retraining the model using actual user queries instead of synthetic data.

• Running A/B tests on different AI models to optimize decision-making capabilities.

Cost to Build AI Agents in 2025

Let’s take a look at the cost to build AI agent phase wise. Different activities occur in each phase (planning and data collection, deployment and maintenance). Below is an estimate of the costs associated with each development phase:

As for developing an AI agent system, its total cost can range from around $20,000 to $60,000 for basic to moderately advanced AI agents. However, if high end enterprise systems with rich feature set and scalable architecture are considered, these ranges may extend.

If you want to optimize the cost of building AI agents, you should get in touch with companies like Biz4Group that offer enterprise AI solutions and have experience in building solutions like AI agents.

Challenges in Building AI Agents & How to Overcome Them

A major lesson learned from our years of experience in custom AI Agent development is that AI agents tend to hallucinate responses when uncertain.

In our recent project, we’ve fixed these challenges like this:

• Defined strict knowledge boundaries to prevent the AI agents from accessing & generating inaccurate information.

• Used reinforcement learning with human feedback (RLHF) to gradually improve response accuracy.

A real-world case study of an AI agent for insurance sales training found that AI agents realized 70% faster agent onboarding. The training cost was also reduced by 40%.

Ensuring AI Agent Ethics and Safety

With years of experience in AI agent development, we’ve found that AI bias is one of the biggest challenges in agent development. For example, AI models can unintentionally favor certain demographics if the AI agent training didn’t use diverse datasets.

Here is how to Prevent AI Bias:

• Train your AI agents on varied datasets; i.e., different languages, cultures, and age groups.

• Test AI decisions across multiple demographic groups.

• Create transparency reports that show how AI agents make decisions.

To avoid discriminatory outcomes, major tech companies like Google and OpenAI conduct bias testing before deploying AI systems. We recommend that your AI agent should follow similar responsible AI development practices.

How to Measure Your AI Agent’s Performance

Just like any digital tool, AI agents should be optimized based on performance data. Here are three critical KPIs you should track:

• Task Completion Rate: Measure how often the AI agents successfully resolve user queries without human intervention.

• Response Latency: The time the AI agent takes to generate an answer. A lower latency improves the user experience.

• Fallback Rate: The rate at which AI fails to respond and hands off a task to a human agent. Reducing fallback rate means a more reliable AI agent.

Just to put a benchmark, a well-optimized AI agent should aim for a task completion rate of 85%+ and response latency under 1 second.

Conclusion

The process building AI agent involves setting goals, defining the operational environment, and collecting required data in an orderly manner. Selecting the relevant AI development platform is one of the key aspects for a project. Through a thoughtful analysis of your options, and the integration potential, scalability and support, you can select a platform that fulfills your requirements, and helps you develop different AI solutions.

From coding the AI agent, and documentation to deployment, every step of this process requires high-precision user experience, and scalability direction. Ongoing monitoring and maintenance guarantee the agent's efficacy.

FAQ

1. How to deal with AI agent hallucinations?

How to Prevent AI Agents from Hallucinating

• Be Explicit in Prompts – Direct AI to say "I don’t know" instead of guessing.

• Use Advanced Models – Opt for AI with better contextual accuracy.

• Lower Temperature – Reduce randomness for more factual responses.

• Improve Prompts – Structure queries to guide precise answers.

• Filter Inputs – Screen ambiguous queries to ensure reliable outputs.

2. Can we use LangChain or LanGraph as a framework for agents?

• LangChain – Initially supported AI agents but has deprecated traditional agent frameworks. Now best for LLM-based applications.

• LangGraph – The new recommended framework for building AI agents with structured workflows and state management.

For AI agents, use LangGraph!

3. For an AI chat querying 10 related SQL tables (one with 200K records), should I use RAG or LLM agents?

RAG is the better choice!

Why?

• Retrieves relevant data from SQL before generating responses.

• Handles large datasets efficiently without overloading the LLM.

• Ensures accuracy by pulling real-time data instead of relying on model memory.

Use RAG with SQL connectors for structured data retrieval!

4. What's the worst part of building agentic systems? What takes more time?

The hardest part is mapping the problem—determining if it can be solved and how to approach it. Once that's clear, the rest is mostly routine.

You’d love to explore open-source models more, but commercial models are so effective that they work for almost any implementation.

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