5 Expert Lifehacks for Building an AI Agent in 2025

AI agents, a more advanced form of AI, are one of the key trends in 2025. 78% of developers and business leaders have active plans to implement AI agents in the near future, LangChain reports.

If you are interested in building one yourself, you need to consider a few key things before starting. Firstly, what kind of AI agent do you want to build?

There is a wide range of autonomous AI agents, including reflex agents, model reflex agents, goal agents, utility agents, and learning agents. These progress from the most basic (reflex), which simply reacts to the environment and inputs based on pre-programmed rules, to the most complex – the learning agent, which, as the name suggests, learns the more interactions it has and improves with the more data and environmental stimulus it intakes.

So, which one do you need? Our step-by-step guide will give you some actionable insights into building an AI agent tailored to your business needs.

Table of Contents Table of Contents

1. Key Takeaways
2. What Is an AI Agent?
3. Adoption Plans & Challenges
4. Step-by-step Guide for Building an AI Agent
5. The Bottom Line
6. FAQs
7. References

Show Full Guide

Table of Contents

1. Key Takeaways
2. What Is an AI Agent?
3. Adoption Plans & Challenges
Show Full Guide
4. Step-by-step Guide for Building an AI Agent
5. The Bottom Line
6. FAQs
7. References

What Is an AI Agent?

Before we consider how to build an AI agent step-by-step, let us first define what one is.

Priyanka Kharat, vice president of product and engineering at ScienceLogic, told Techopedia:

“Agentic AI will enable IT teams across all industry verticals to reimagine complex operations by breaking them down as goals that will be planned, adapted, and taken through decisive actions autonomously, with minimal human intervention.”

Although some human interaction may be required, the system is set up to respond to a range of language inputs and then learn continuously through its interactions with the data and feedback it collects.

“Agentic AI tools will deliver timely, persona-based insights and be based on rich algorithmic analysis generated in a matter of seconds, setting the stage to transform traditionally reactive IT systems into proactive and self-optimizing ecosystems,” Kharat added.

Adoption Plans & Challenges

According to a recent study by LangChain, surveying views of 1,300 professionals – from engineers and product managers to business leaders and executives – the adoption of AI agents is ‘heating up.’

However, while the demand for AI agents is increasingly strong, the actual deployment might face some challenges. LangChain stated:

“Performance quality stands out as the top concern among respondents – more than twice as significant as other factors like cost and safety.”

Consider the following fundamental steps, which might help to overcome these barriers and build reliable and controllable AI agents for your business.

Step-by-step Guide for Building an AI Agent

Step One: Define Purpose & Scope

As with the key tenets of project management, you need to create a clear purpose and scope for the project, or in this case, the AI agent. What do you want it to do?

Be as specific as you can with the tasks and functions you want the AI agent to handle. Are you designing it to answer simple customer queries, or are you creating a virtual shopping assistant?

List the problems you want the agent to solve, and then the functionality can be mapped onto this.

You should also have a target audience in mind so you personalize the system as much as possible while still making it accessible to a large audience.

You will also need to formulate clear objectives. This will help you tailor the system accordingly and is the only way you can clearly judge whether it has worked.

If you want to increase the success rate of customer queries being resolved or increase sales through a more elaborate assistant, it’s important you create tangible targets you can reference later.

It’s much easier to build an AI agent, if all you have to do is prompt it!

The backend, code execution and deployment are all handled automatically.

Even non-techies can build AI agents pic.twitter.com/dDXoGuNnRB

— Bindu Reddy (@bindureddy) November 25, 2024

Step Two: Prepare Training Data

The next step to follow when building AI agents is all about data and training.

Consider the data as the raw ingredients you are adding to a recipe. If they are subpar, the dish will not turn out well. If the data you train your AI agent app is of low quality or riddled with errors, the finished agent will make mistakes.

The data you collect and use needs to reflect the agent’s functionality. Collect the relevant datasets from as diverse a range of sources as makes sense for your purposes, then utilize data visualization tools to clean it and get it ready to input into the system.

Using the example of a customer service agent, inputting transcripts or recordings from customer service calls will prime the agent for the interactions it will face once deployed. If it will be responding to live audio, use voice recordings with many different accents and speech patterns to ready the agent for real-life scenarios.

However, if it’s a text agent, simple transcripts will work better. How people ask questions can differ similarly to how they speak, with spelling and grammar often input incorrectly. Therefore, exposing the system to many variations will make it easier for the system to recognize the meaning, even when it’s not expressed perfectly.

New short course on Pretraining LLMs! Developed with @UpstageAI and taught by their CEO @hunkims and CSO @echojuliett.

While prompting or fine-tuning existing models works well for many general language tasks, pretraining is valuable for specialized domains or languages with… pic.twitter.com/388WZkjEwm

— Andrew Ng (@AndrewYNg) July 17, 2024

Step Three: Choose Your Model

To establish how effectively your AI agent is able to learn from data, you need to choose a machine learning model.

You can opt for a neural network, which is designed to reproduce the way our brains work. This will work well for systems processing large amounts of data, enabling them to recognize patterns and generate human language.

The other option is a reinforcement learning model. This will enable the system to learn through trial and error, improving over time through ongoing user interactions and feedback.

Using our brain simulator, we’ve trained a reinforcement learning agent to maximize bits per second. Here is the RL policy converting brain data to cursor control in simulation: pic.twitter.com/i4QuCZRUwp

— Neuralink (@neuralink) January 17, 2025

Step Four: Training & Testing

This is the step where generative AI agents actually learn how to perform tasks.

Before inputting any data, you will need to iron out your training parameters. This includes the learning rate and batch size, which determine how much the model adjusts during the training process.

Now, you can begin inputting data into the model and monitoring its performance. For the best results, you will want to fine-tune as you go to ensure you consistently get reliable outcomes by the end of the training stage.

Following the training phase, it’s essential to test and validate what the agent has learned. This will catch any issues missed during training and ensure the deployed version is bug-free.

Put the system through its paces with a series of tasks or queries that mimic what it will face when deployed. You may want to go slightly beyond the narrow focus area to make interactions flow and smooth out the user experience.

Measure how the system performs and then adjust accordingly for further training and testing or validate and progress to the next step.

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— DeepLearning.AI (@DeepLearningAI) January 24, 2024

Step Five: Deployment

If you’ve made it this far, you’ll be pleased to know you are now ready to release your AI agent into the wild. This means deploying it into a live environment with real users.

This could be on your website or mobile app; wherever you plan to use it, ensure your support systems are up and running so the launch is as smooth as possible.

Once deployed you will still need to regularly check how it is performing. Are simple tasks being handled quickly and efficiently? Does it hold its own in more complex interactions? Monitor response times and user satisfaction to ensure that it is performing well for your business and the customer.

Continue to collect data and feedback. A core component of an AI agent is its ability to improve over time.

Frameworks for deploying agents have surged in the past few months.

LangChain reports that 78% of developers and PMs have active plans to implement AI agents in the near future.

Compare frameworks like REI, Eliza, RIG, and more in our latest report. pic.twitter.com/E7UbXA9sUT

— Messari (@MessariCrypto) December 27, 2024

The Bottom Line

Hopefully, you now feel like you know how to build an AI agent. A few additional things to bear in mind are the need for clear goals and a defined structure. Too many instructions and undefined boundaries are the enemy. Rather than expanding the capabilities of your agent assistant, they will overwhelm the system and make it harder for it to perform the core functions you outlined.

You are also under no obligation to start from scratch. You can utilize an AI builder to do a lot of the legwork for you. There are trade-offs to this approach, such as less autonomy and a more generic agent, but it will take less time and money to deploy.

FAQs