How to Build an AI Agent for Better Automation

A Capgemini report shows that 50% of US companies now use Generative AI for their marketing projects. While these tools are evolving, they can’t streamline multi-stage workflows or handle large-scale data without frequent human input.

But what if you could gain that competitive advantage today? What if there’s a way to automate complex processes right now?

Enter AI Agents—the next stage of AI implementation for businesses!

Knowing how to build an AI agent is crucial in the long run. Once you know how to build a custom agent, you can automate tasks (like customer service or market analysis) with little human intervention and reduce overall costs.

In this blog, we’ll answer everything about AI agents, from what they are to how you can develop one. Stick around to the end—we’ll reveal an AI agent that’s efficient and seamless for your task and project management needs!

How To Build An AI Agent for Your Business

⏰60-Second Summary

AI agents are autonomous AI tools with decision-making capability

They can interact with humans and tech tools in their environment

AI agents are already being used in e-commerce, healthcare, business process automation, and cloud computing industries

You can build a custom AI agent with data scientists, UX designers, machine learning, and software development experts

If you use ClickUp for project management, you’ll already have a built-in internal AI agent at your disposal

Let’s tackle the basics first.

What is An AI Agent?

If you’ve ever chatted with an AI assistant on a website, you’ve already interacted with a basic AI agent. The most common places to find them today are on companies’ support pages, answering customer queries, creating support tickets, or arranging calls with live support agents.

However, an AI agent’s capabilities are not limited to managing customer support alone. It can do a lot more, as you’ll see below.

Definition of an AI agent

An AI agent is an autonomous program that performs pre-defined functions with minimal human intervention. It can recognize and interact with different actors and elements in its environment to help you achieve your goals.

For instance, if you want to send someone an email, an AI agent can take the necessary inputs from you, such as the recipient’s email address, email topic, file attachments, etc. It then interacts with your email client to draft the email on its own using generative AI.

Once done, it shows you a preview of the email so you can change anything if required and send it once the changes are made.

Key characteristics of AI agents

Here’s what you need to know about AI agents in a nutshell:

Minimal human input requirement
Continuous learning and improvement
Context awareness and ability to interact with their environment
Ability to read, extract, and modify data from external sources
Understanding of human language and behavior
Ability to make decisions based on their training and learning

Types of AI agents

You can categorize AI agents based on various elements (i.e., design vs. functionality). Here, we’ll categorize them based on functionality, which brings us to two major types of agents that are seen prominently across organizations nowadays:

Autonomous AI agents: These agents are usually customer-facing and have a high level of autonomous decision-making capability. They manage customer queries without requiring human intervention from your employees.
Assistive AI agents: These are internal AI-powered apps that assist your employees in completing complex tasks. Since they are internal, they may or may not have a graphical UI, depending on your preferences.

How to Build An AI Agent

Building AI agents isn’t difficult but requires a structured approach and proper planning. Here are eight steps you must follow when you build custom AI agents for your business requirements:

Step 1: Define the purpose of your agent

Before you start building your own AI agent, you must clearly define what you want to achieve with it. And we’re talking about formal documentation.

Sure, you may have a rough idea of what you want the AI agent to do, but to ensure that nothing is missed, you must document all the functions and capabilities you want in it.

Additionally, it creates a central document to which your development team can refer when they want to understand the agent’s environment and expectations.

Step 2: Build a team

The next step (and it’s a crucial one) is to put together your team to build the AI agent. Even if you’re a software developer, DO NOT attempt to build AI agents all by yourself. That’s because building a robust one requires expertise from different fields, including:

Data science and analysis
Machine Learning (ML)
UI design
Software development

Until you engage professionals from all these fields, you might end up building a flawed AI agent. Instead, assemble a team of experts first.

Step 3: Identify your tech stack

Once you’ve put your team together, you should discuss and decide on the technologies that will serve as the platform for your AI agent. This includes:

Programming language (Java, Python, etc.)
Hosting environment
Natural Language Processing (NLP) libraries (Gensim, NLTK, etc.)
Data analysis libraries (Plotly, SciPy, NumPy, etc.)
ML model (i.e. GPT, BERT, Llama, etc.)
Technologies based on specific capabilities (i.e., computer vision, speech recognition, robotic process automation, etc.)

You should also leave some room for other libraries and frameworks that might be required.

Once you identify and select all these elements for your AI agent’s tech stack, you’ll have a strong foundation to build on.

Step 4: Make your design considerations

Besides identifying the tech stack you want to use, there are design considerations that you should take into account before developing AI agents. They include:

1. Architecture

There are two approaches you can take for the architecture of your custom AI agent—modular and concurrent. In modular architecture, each part of the agent is designed sequentially and separately before being put together to finalize the agent. Concurrent architecture, on the other hand, is one in which all parts are trained and built at the same time.

2. User interface and experience (UI/UX)

If you want your AI agent to have a public-facing user interface, then you should also consider the elements you want to include in UI/UX. This includes your branding, a mascot, a name you want to give to it, etc.

3. Data handling

How your custom AI agent receives and works with relevant data is another crucial consideration you should make. This means clearly defining the entire data flow from start to end, including:

Data/information to be received from the user
Data/information to be extracted from your server
Functions to be performed on extracted data
Delivering the end result to the user

Each step in the data handling process should be laid out in detail.

4. Feedback mechanism

Consider including a feedback mechanism in your AI agent system. Whether it’s a survey, a rating method, or a simple like/dislike button. It’s essential to receive feedback about the agent from users to improve the tool continuously.

Step 5: Label and clean your training data

There are three kinds of data sources you can use to prepare and train your agent, depending on who will be its end users:

Operational data of your organization
External data you received or acquired from third-party sources
User-generated data that was generated by your customers/users

Regardless of whatever raw data you choose to train your model on, it must be labeled and cleaned before training. What’s labeling and cleaning? Well, here’s a brief overview:

Labeling: This refers to the manual categorization, tagging, and labeling of data by humans to make it understandable for your AI agent. It’s done so the AI model used in your agent can build connections between data points and correctly recognize what each type of data represents.
Cleaning: This refers to removing any anomalies from your dataset, such as empty rows, misrepresented or missing values, errors, etc. Removing them eliminates the possibility of your AI agent being trained on flawed data.

💡Pro Tip: Tools like SuperAnnotate, DataLoop, and Encord help you with both these steps.

Step 6: Build and train your agent

Now, you can start building and training your AI agent. Start by setting up your training environment—install all necessary ML libraries and frameworks, start the training tools, and load your data.

⚠️ IMPORTANT: Don’t load all your data at once. Divide it into two subsets and load only one. Keep the other set for testing purposes.

Once you have loaded your data, initialize the ML model you selected in step three. Set the training parameters (they can vary depending on your chosen model, so it’s difficult to go into specifics here), and start the training process.

Track metrics like loss and accuracy during the training process to get an idea of how well the model is learning. If it’s not learning well, tweak the training parameters.

At the same time, UI developers should design and build the UX of your AI agent.

Step 7: Test the agent

Once the training process is complete, it’s time to test your model. This is where the other half of your dataset that you reserved for testing purposes (Step 6) will come into the picture.

Start your AI agent, run it through the queries of your testing dataset, and analyze the results. Observe how accurately it performed the desired function on each data point in your dataset. Also, observe how long it took to perform those actions.

If the agent works as intended, there are three more types of tests you must perform on it. These are:

Unit tests: Test each module or unit of your AI agent independently to ensure that they function properly
User tests: Invite some target users of the agent to try it under your observation so you can analyze how users might use it and how accurately it works in each scenario
A/B tests: Compare two versions of the agent side-by-side to see which one does the job better

Each of these tests will optimize your AI agent’s performance and ensure that it performs well in real-world scenarios. However, if it doesn’t perform well during the tests, you may have to retrain the agent with adjusted parameters or a bigger dataset.

Step 8: Deploy and monitor the agent

Finally, once your AI agent is working as intended, it’s time to deploy it. Integrate it with your existing systems and deploy it on your website or app. Monitor how accurately and quickly it responds to user queries by analyzing user logs and feedback that comes through the built-in feedback mechanism of your AI agent.

If there’s any room for improvement, roll out a new version of the agent by addressing the issues flagged by users.

Implementation and Use Cases of Custom AI Agents

The use cases of AI (particularly its agents) are immense in each industry. There are four major areas where they are currently leaving their mark.

1. AI agents in e-commerce: AI consultants and customer service agents

The AI agents in e-commerce companies generally aim to achieve two key objectives:

Predicting demand fluctuations: By analyzing historical sales data and market trends, the e-commerce AI agents predict demand fluctuations and help their businesses stay ahead of the curve
Handling customer support tasks: E-commerce AI agents also analyze customer interactions for accurate resolutions

Example: Shein’s Virtual Assistant is an excellent example of using an AI agent to assess changing market trends. In fact, it lists up to 600,000 items based on consumer needs, all for a global market!

2. AI agents in healthcare: predictive maintenance and virtual assistants

AI agents can help healthcare companies prevent equipment failures by continuously monitoring and analyzing the health of medical devices. This increases equipment life and also alerts the organization when it’s time to replace the devices.

Additionally, AI-powered virtual assistants and chatbots are helping patients with follow-up reminders and appointment scheduling. They can even analyze medical data for treatment suggestions and help doctors with a diagnosis. See how. 👇

Example: IBM Watson Oncology acts as a proactive AI agent in the field of cancer treatment. Designed to assist oncologists in making informed decisions, it analyzes patient data, extensive medical literature, and relevant clinical trials to generate evidence-based treatment recommendations.

While ultimately requiring physician input, Watson Oncology proactively presents potential treatment options and highlights pertinent research findings, thereby actively contributing to the decision-making process by providing crucial information.

3. AI agents for business process automation: recommender systems and robotic process automation

Businesses prefer using AI agents for task automation when working with Robotic Process Automation (RPA) tools. Examples include:

Automatic claim settlement by insurance companies using computer vision and data analytics
Fraud detection and automated blocking of fraudulent transactions in financial companies by analyzing historical data
AI and ML-driven automated document classification based on previous data

Example: Fukoku Mutual Life, an insurance company in Japan, uses AI agents to process claims. With AI, it can access medical insurance and automatically calculate payouts. This led to the company saving almost $1 million in costs and increased their employees’ productivity by 30%.

4. AI agents in cloud computing and automation

AI Agents can help cloud computing and automation companies with resource planning, security monitoring, and customer support activities. They do this by:

Predicting computing power requirements
Analyzing and monitoring suspicious user activity
Understanding customer queries using NLP before responding with answers from an AI knowledge base

Example: Amazon Web Services (AWS) is a standout instance of using AI agents to predict computing power requirements. Using historical data, its AI systems efficiently allocate resources and save costs. This ensures that even with usage spikes, AWS’s systems don’t face any downtimes.

The AI Agent for Your Project Management

Remember when we said that we’ll reveal an AI agent at the end? Oh, and did we mention that you don’t even need to build it? Simply put, if you need AI for productivity, this is the shortcut to a competitive edge.

This AI agent is ClickUp Brain—an AI that powers all of ClickUp’s features and maximizes your productivity. It integrates seamlessly with your app stack, automates your workflows, and eliminates the manual aspects of project management.

But ClickUp Brain doesn’t just handle automation—it works as your super-smart project assistant. From identifying bottlenecks to smart work scheduling based on your team and their workload, it manages everything you need to optimize your project’s progress.

This AI-powered productivity is also weaved into ClickUp Chat, its built-in messaging platform for real-time collaboration. Thanks to ClickUp Brain’s advanced AI, ClickUp Chat enriches your conversations with information from all your projects, tasks, docs, and more.

It’s capable of summarizing your chats, fetching the relevant project information, and creating tasks for your team members.

ClickUp Chat: how to build an ai agent — Bring work and workplace communication together with ClickUp Chat

Here’s a brief list of all that’s possible when you use ClickUp Chat as the AI-powered agent for your workplace:

Fetching information from other apps: Looking to find a file from your Google Drive and link it to a task? Use the relevant prompt in ClickUp Chat (just remember to connect the Drive to your ClickUp account)
Quick catch-up: Been away for a while? Click on Catch me up to get a short summary of the thread
Task creation: Need to create a task while on call with a teammate? You can do that without leaving the chat window. You can also link each task to the target project and concerned team members automatically using AI

Share meeting minutes with your team within ClickUp Chat and assign action items to respective team members with its AI capabilities

Advantages of using ClickUp Chat as your internal AI Agent

There are many advantages to using ClickUp Chat as your organization’s internal AI agent. However, the major ones that stand out are:

✅ Streamlined workflow: Link tasks and docs within chat to avoid switching between apps to manage your work and related conversations

✅ Improved efficiency and productivity: Supercharges your and your team’s productivity with tasks, views, dependencies, announcements, and discussions easily accessible from within Chat

✅ Better data privacy and security: Keep your project management data in one place, protected with the best security standards

Make Work Effortless With ClickUp Chat As Your AI Agent

Business processes—like task management or customer service—will most likely be handled by advanced AI agents soon. It won’t be long before you see enterprises implement custom agents for their routine tasks and workflows.

Want to get ahead of the competition but don’t want to spend resources on building custom AI agents just yet?

ClickUp’s readymade offering, ClickUp Chat, effectively handles team collaboration and project management, transforming your business processes through a centralized system.

If you’re ready to maximize your business efficiency, sign up for ClickUp for free!

Everything you need to stay organized and get work done.

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How to Build an AI Agent for Better Automation

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⏰60-Second Summary