How to Create AI Agents in Minutes: Quick Guide
Every day, more companies rely on AI agents to handle tasks that once needed constant attention from people. These agents can handle routine tasks, answer customer queries, and even provide information about your business so your team can focus on bigger goals.
Not long ago, building AI agents required advanced machine learning skills and complicated systems. That’s not the case anymore.
In this article, you’ll learn what AI agents are, the steps for building effective agents, and how Activepieces makes the process easier.
Get started for free and watch Activepieces build AI agents in minutes!
What Are AI Agents? Understanding the Core Principles
An AI agent is basically a program that assesses the situation to determine necessary actions, and then actually does them. That’s the difference between an ordinary bot and an agent. A bot just follows a script, but an agent adapts depending on the situation.
These work through a loop called OPA: observation, planning, and action.
First, it takes in some input, such as a message from a user, data from a system, or even a live feed from sensors. Then it plans, which means breaking the job into steps and picking the right tools.
Sometimes, that even includes creating a little conversation flow so it can be used to talk to someone in a way that makes sense. After that, it acts. That might be sending an email, updating a database, or passing part of the job to another agent.
You see this in practice every day:
- Support agents who handle tickets.
- Supply chain agents that reorder stock.
- Autonomous agents that help run self-driving cars.
There are also agent platforms that let you build and track these agents with dashboards, so you can actually see how well they are working.
Why Build AI Agents
Many businesses build AI agents since they can do things that regular software can’t.
Traditional programs usually just follow a fixed set of rules. You give them an input, they follow the script, and that’s it. An agent, on the other hand, has the capabilities to take in what’s happening, plan out the next steps, and act alone.
Even when you run an agent 24/7, it doesn’t get tired and doesn’t make the same mistakes people often do. In supply chains, for instance, an agent watches stock levels, predicts shortages, and reorders items before you even notice an issue. That’s inventory management done automatically.
And you don’t always need heavy infrastructure to set them up anymore. With newer tools, you can get started quickly, build agents that fit your needs, and keep scaling as your business grows.
6 Steps to Build AI Agents With Activepieces
Follow these in order, and you’ll be able to create agents that handle well-defined tasks and fit smoothly into existing workflows.
Step 1: Sign Up and Log In
The first step is, of course, to sign up on Activepieces. Once you sign up and log in, you’ll land on your dashboard, where the building begins.
At this stage, it helps to know which plan fits your needs. Activepieces offers a free, cloud-based option that includes 1,000 tasks per month, AI steps and agents, 200 AI credits, two active flows, and community support.
If you’re part of a small team, the Plus plan at $25 per month gives you unlimited tasks under a fair use policy, over ten active flows, unlimited tables, 500 AI credits, and email support.
For heavier use, the Business plan at $150 per month includes unlimited tasks, 50 active flows, 1,000 AI credits, ten projects, API, and five users.
Enterprises can request a custom plan with dedicated support and resources. There’s also a free, open-source Community Edition you can self-host if you want full control over your data.
Compared to Zapier or Make, where costs climb with task counts, Activepieces keeps pricing flat and predictable.
Launch your first automation today and try Activepieces for free!
Step 2: Open the Agents Tab and Create a New Agent
After logging in, open the “Agents” tab and click on “New Agent” on the right side of your dashboard. This is where all the building happens. Think of it as a workspace for creating, testing, and managing your AI agents in one place.
Rather than writing the same instructions in every workflow, you can store them here and reuse them whenever needed. Each agent gets its own profile with a name, description, and a clear mission that defines what it should do.
The tab also lets you test outputs before going live, so it’s easier to fine-tune your setup. From automating customer support to building content workflows, the use cases you can cover start right here in the “Agents” tab.
Step 3: Name and Describe the Agent
When you set up a new agent in Activepieces, you’ll want to give it a name that makes sense right away.
Naming it something like “Customer Support Agent” or “Blog Writer” helps you and your team avoid wasting time figuring out what it’s for. That becomes even more important once you have a few agents running in the same account.
The description is where you set its mission. You can write out:
- Who is the target audience for this agent?
- What role should it take on?
- How should it act?
For instance, “Act as a support assistant for customers who submit technical issues.” That single sentence already gives the AI a clear direction.
Adding detail in the description also keeps it focused. You’re giving the system relevant information so it doesn’t wander into tasks you don’t want it to touch. Later, when you look at logs or need to debug, a clear name and description make it easy to see what happened and which agent did the work.
Step 4: Add Instructions
Inside the setup screen, you’ll find a text area where you type instructions. That’s where the agent gets its purpose, scope, and behavior.
The most successful implementations focus on specific, well-defined tasks, so don’t overload the agent with too many goals at once. For example, a customer support agent might only classify tickets, respond with short answers, and escalate urgent ones.
In a few lines of clear text, you can lock in the agent’s role and tone. Something like, “You’re a support bot who answers questions in a friendly but concise style. When the query is technical, send it to the IT team,” gives enough structure to keep results consistent.
Prompt engineering matters here. The way you phrase the role shapes how the model processes requests and decides which tools to use.
Action verbs such as “categorize,” “summarize,” or “draft” make the guidance direct and easier to follow. After writing your first draft of instructions, run a quick test, see how it responds, and adjust if the output isn’t where you want it.
Step 5: Add Tools
Click “Add Tool” and choose either “piece” to pick from over 423 integrations or from “flow” to turn an existing workflow into a tool your agent can use.
The option depends on what your agent needs to do or the user’s preferences. Some setups call for certain tools like Google Sheets or Gmail, while others might rely on custom flows.
From Piece
Connect your agent to the apps and services it needs. In Activepieces, these integrations are called “pieces,” and right now, there are 423 ready to use.
You’ll find familiar tools across almost every category, such as:
- OpenAI
- WooCommerce
- ElevenLabs
- Microsoft Excel 365
- Google Sheets
- Motion
- Slack
- Discord
- ClickUp
- WordPress
- ActiveCampaign
- Zoho CRM
- Stripe
Since Activepieces is open source, the community contributes new pieces all the time, which means the library keeps expanding. Developers can even write their own in TypeScript and push them into the ecosystem.
On top of that, it takes an AI-first approach, so your agents can tap directly into these pieces to build smarter workflows. In short, pieces are the bridge between your agent and the apps it needs to get real work done.
From Flow
Once your agent is ready, bring it into a workflow. Start by creating a new flow from the dashboard. Add a trigger, which is the event that kicks things off. For example, you can use a Zendesk “New Ticket” trigger or a webhook from another app.
Add an “Agent” step to the flow. Choose the agent you just made, then fill in the input field to tell the agent what to do with the data coming from the trigger.
After the agent does its job, chain more steps. If the output is “Billing Problem,” you could route it to a finance tool. When it’s “Technical Issue,” maybe create a task in your dev tracker.
Finally, run a test. Check how the agent behaves with real data. As you notice that it doesn’t perform the way you want, go back to its instructions or permissions and refine. Over time, you’ll shape an agent that consistently delivers the right results.
Step 6: Deploy and Refine Your Agent
The deployment starts when you add it to a flow and connect it to the right inputs and outputs. For example, you might feed new data from a Google Sheet into your agent with the prompt, “Draft a personalized email for (prospect_name).”
From there, link its output to the next step, like sending the draft through Gmail. Run a quick test to confirm it’s pulling the right input and producing the response you want.
After you launch the AI agent to start interacting with users, the real work begins. Refinement comes from observing how it performs in live runs. Review logs to spot gaps, such as observed errors during data processing, and use that insight to adjust instructions or tool usage.
Collect user feedback afterward to refine your AI agent, especially in customer-facing workflows, since this highlights where clarity or tone needs improvement.
Example of Agents You Can Build Today
AI-Written Reminders
Sending reminders manually takes time, especially if you’re managing a long list of subscribers. With Activepieces, you can automate the process so every customer gets a timely, personalized message.
Steps to build it in Activepieces:
Add a trigger that runs weekly or monthly based on your needs.
Connect your Google account and select the right sheet.
Pick the start row, usually row 2 if row 1 has headers.
Decide the group size for how many rows you want to process at a time.
Loop through each row so the flow handles every user in sequence.
Add a Text AI step and choose an AI provider like OpenAI.
Write a prompt that generates reminders using details such as the customer name and renewal date.
Test the step to make sure the AI creates clear reminders.
Connect your email provider for delivery.
Run a final test before going live.
Once set up, the system runs on its own. The agent reads the data, writes tailored reminders, and sends them on time.
Create AI Agents That Deliver Real Results With Activepieces
Activepieces helps you build agents that actually deliver results without all that lengthy manual work.
Right now, there are 423 integrations available, and that number grows every day as the community contributes new ones. You’re not limited to a small set of apps, so your agents can connect with whatever your business depends on.
For non-technical teams, the no-code builder means anyone can set up a flow in minutes. For developers, every piece is written in TypeScript, which means you can fully customize and even hot reload changes while building.
It also comes ready for AI work. You can create agents with built-in AI pieces, connect them to providers like OpenAI, and even use AI Copilot inside the builder to guide you step by step. For security, you can self-host for maximum data control or go to the cloud.
In short, Activepieces helps you cut manual tasks, save costs, and run smarter automations that grow with your business.
Get in touch with sales today and discover how Activepieces can power your automations!
FAQs About How to Create AI Agents
How much does it cost to create an AI agent?
The cost ranges from free plans, like Activepieces’ 1,000 free tasks per month, to $25 or $150 per month for higher usage, with enterprise pricing available. The final price depends on how many flows you run, what integrations you need, and the level of access your team requires.
Can you build your own AI agent with ChatGPT?
Yes, you can build your own AI agent with ChatGPT by writing clear instructions and giving it the right context. It works best when combined with tools that let you connect it to external apps so it can do more than just answer questions.
Can you make AI agents with Make?
Yes, Make (formerly Integromat) supports building automations that act like AI agents. However, Activepieces provides more pre-built connectors and is easier to integrate with existing workflows, which saves time for both developers and non-technical users.
What are the different use cases of AI agents?
AI agents can handle customer support, manage inventory, automate marketing, process data, and personalize content for users. Most platforms describe these use cases on their website, so you can see exactly how agents can fit into your business.
Is natural language processing (NLP) important when building AI agents?
Yes, natural language processing (NLP) is important when building AI agents because it allows them to understand, interpret, and generate human language, which is essential for handling user queries, following instructions, and maintaining clear conversation flow.