How to Build an AI Agent with UiPath Agent Builder: A Step-by-Step Guide to Get Started

We are already at a stage where enterprises are eager to learn, explore, and implement AI agents in their automation workflows. UiPath is at the forefront of this shift, offering innovative ways to automate processes with intelligent AI-driven solutions. With the introduction of low-code platforms, it is now easier than ever to build and deploy AI agents seamlessly.

But before we get into building reliable, scalable and secure AI agents, let’s first understand some key terminologies in the most simple way.

Understanding the Basics: Key Terms You Need to Know

These terminologies are the core of building an AI Agent. Understanding them can help you fast track the process with confidence.

• Agentic AI: Just like Robotic Process Automation (RPA), Agentic AI is technology automation in action. It makes decisions, learns from interactions, and takes action accordingly.
• Agentic Automation: When Agentic AI is applied in process automation, it becomes Agentic Automation, allowing workflows to be more adaptive and intelligent.
• AI Agents: AI Agents are autonomous entities that can understand, act, and perform complex tasks just like robots in RPA but in a much smarter way.
• Agent builder: UiPath Agent Builder is a low-code platform that enables you to build and deploy AI agents, similar to how UiPath studio is used to build RPA bots.

Core Capabilities of AI Agents

AI Agents possess unique properties that make them powerful, more reliable and efficient than the traditional bots. With these capabilities, AI agents are not just rule-based automation tools; they’re intelligent, learning systems that evolve with use.

• Creative Thinking: Generates intelligent responses.
• Goal-Oriented: Works towards achieving specific objectives.
• Intuitive: Learns from past interactions.
• Self-Adaptive: Able to adapt to situations from self-learning ability.
• Handles Uncertainty: Can handle ambiguity and make decisions even with unstructured data.
• Independent Action: With a simple prompt they can execute tasks without constant human input.
• Context-Aware: Retains memory for better decision-making.

What Are the Key Components of Agent Builder?

There are four core components of Agent builder:

1. Natural Language Processing (NLP)

AI Agents use NLP to understand, interpret and generate human-like responses in simple language through Prompts. Internally, the agents are using Large Language Model (LLM) for text processing, text generation, summarization, translation and classification like activities.

Understanding Prompts: How Agents Communicate

UiPath Agents accept two types of prompts

System Prompt:

This prompt is for agents to understand its role, objectives and instructions. While designing the agent we should be clear goals on what and why we are building that agent to ensure that it functions as expected.

Example:

I want to build an agent for creating travel plans for me. In this case my system prompt will look like the below:

#Role

You are my travel management agent

#Objective

Your goal is to provide me with a detailed plan according to the input I provide

#Intructions

Collect travel details (source, destination, dates) and generate a structured itinerary with the given information - mode of transport with time required and cost, places to visit, >3-star hotel to stay with price with breakfast included, best vegetarian food to eat in the city. Generate output in table format.

#Tools

Send details in email using “Automated_Travel_Plan” process

User Prompt:

In simple words, system prompt is how user is going to ask the agent a question to generate desired output.

Example:

For the travel agent described above, the user prompt will be:
Generate a travel plan from New York to Paris for 4 days from April 10 to April 14.

Additionally with this prompt, you can pass input and output parameters to provide agent with necessary data it will require to process and get desired output in a specific format.

Example: For travel agent my input will be Source place, destination place, travel start date and travel end date.

Learn the best way to build Agents with prompts—watch now!

2. Context Grounding

LLMs are trained on vast datasets, which can sometimes result in generic responses. To get context-specific answers, we need to provide reference documents.

Example: If I ask, “What are the different leave policies?”

This is the output it generates:

However, my organization’s policies may differ. So, I need to set up a context for my agent that I want to know my organization’s leave policy. But how will the LLM know my policies? We need to provide documents to set up context, and it will refer to the information within those documents to answer my queries.

3. Tools & Integrations

Tools can be referred to as anything you can integrate with your agent to:

• Retrieve data from enterprise systems
• Trigger automated workflows
• Send emails or notifications

They can also work alongside RPA bots and other automation tools for seamless execution.

4. Human-in-the-Loop

Human plays a pivotal role in the automation process. For some tasks, agents might require human intervention, such as based on a complex scenario:

• Approving or rejecting decisions
• Handling exceptions
• Reviewing certain outputs

As agents learn from human interactions, they improve their decision-making abilities over time.

Other Significant Components of Agents for Configuration Settings

1. Temperature to Adjust Output Creativity

Controls how precise or creative the AI agent should be in their response.

• Low Temperature → More structured, factual responses
• High Temperature → More creative, varied outputs

Example

• Writing a leave request email? Low temperature.
• Crafting an event announcement? High temperature.

2. Token Generation to Manage Data Processing

Tokens are the measure of how data is passed on to the LLM. Basically, how much data the agent can process at once. Larger datasets require higher token limits. A small token limit means large datasets cannot be provided to the LLM. If you want your agent to use large datasets like large files, you will have to set the token limit to a high number.

3. Playground to Test Agents in Real Time

Before deployment, agents can be tested in a controlled environment to ensure they function correctly.

4. Threshold & Result Filtering

When a context is setup for an agent, we can set threshold value to get result matching to that threshold percentage or score. It means agents can be configured to return results based on accuracy scores.

Example: Setting a 0.7 threshold means only results with a 70% confidence level or higher will be shown.

5. Publishing & Execution Logs

Agents can be published to UiPath Orchestrator, where execution logs (Traces) help in monitoring and troubleshooting.

Prebuilt AI Agents to Help You Get Started Instantly

UiPath provides prebuilt agents for common use cases to get started with. It requires less configuration and is ready to use, allowing businesses to quickly adopt AI-driven automation.

• Modify and customize prebuilt agents to fit specific needs
• Deploy instantly with minimal setup
• Reduce time-to-value by using ready-to-go automation

What You Need to Get Started with Agent Builder?

Before building AI agents, ensure you have:

• ✓ Knowledge of UiPath Studio Web and Integration Services
• ✓ Familiarity with Action Apps and Automation Developer License
• ✓ Access to the AI Trust Layer
• ✓ Prompt Engineering Knowledge

Step-by-Step Guide to Build an AI Agent in UiPath

1. Open Agent Builder

Navigate to UiPath Orchestrator → Select Agents from the left menu.

2. Create a New Agent

Click on "New Agent" to start building from scratch.

This will display below screen to start building agents. You will all components discussed.

3. Set Agent Properties

• Give your agent a name and description.
• Write system and user prompts.
• Set up input and output parameters.

4. Integrate tools (Optional)

Connect the agent with:

• RPA workflows
• API-based integrations
• Enterprise systems

5. Define Context (Optional)

Upload reference documents to optimize contextual accuracy.

6. Add Human-in-the-Loop (Optional)

Define conditions where manual review or approval is required.

7. Test Your Agent

Use the Playground to validate the agent’s functionality.

8. Publish & Deploy Your Agent

Once tested, publish the agent to Orchestrator for deployment.

How Prepared Are You for Agentic Automation?

As many new features and developments are expected to come soon in the Agentic AI space, it is good to have basic knowledge of this new technology to be leaders in Agentic automation. Agentic automation will become a day-to-day part of business operations. So, how to get started?

• ✓ Identify automation use cases that could benefit from AI agents.
• ✓ Define clear goals and objectives for the agent.
• ✓ Start building smarter automation with UiPath Agent Builder.

AI Agents are more than just chatbots—they can make intelligent decisions, adapt, and work autonomously. Partnering with a trusted leader in AI Agents-driven intelligent automation can empower you and your teams with end-to-end process automation powered by expert strategies and roadmaps for faster ROI. Connect with us today!