How to Build an AI Agent

AI agents handle complex tasks and make decisions on the fly, without waiting on you for every step. Using an agent to qualify leads or answer support tickets is quickly becoming the standard: Gartner predicts 40% of enterprise apps will include task-specific AI agents by the end of 2026, up from less than 5% in 2025.

If you’ve been wondering whether you can actually build one yourself, especially without a technical background, the answer is yes. With Bubble, you chat with AI to generate your agent’s foundation, then refine it with AI or through the visual editor, no coding required.

Understanding AI agents

An AI agent is software you can hand a goal to, almost like delegating to a capable assistant. It plans out the multi-step response on its own and adapts as new information comes in, all within whatever limits you set. Most agents come down to a few components working together:

• Model: The large language model (LLM) doing the actual thinking, the part that powers the agent’s reasoning and language understanding.
• Tools: Whatever actions the agent’s allowed to take, like querying a database, sending an email, or calling an external API.
• Memory or context: How the agent remembers things across conversations and tasks, not just in the moment.
• Orchestration: The logic tying it all together, coordinating the model, tools, and memory to work toward a goal step by step.

For example, take something like keeping CRM records up to date. An AI agent can read the emails and Slack threads itself, catch when a deal moves stages, then update the CRM and notify the right person.

Under the hood, agents lean on LLMs to make sense of natural language and reason through what you’re asking for. Then they act: firing off an email, querying a database, summarizing a document, or kicking off a whole set of follow-up actions.

Some agents work quietly in the background. Others sit right in a chat window or a widget inside your app, ready whenever you need them. And some stick to a single, narrow task (like summarizing documents), while others chain several actions and tools together to handle more complex, multi-step work.

What makes AI agents different from chatbots and virtual assistants

People often lump AI agents in with chatbots or virtual assistants, but they work in fundamentally different ways.

• Chatbots usually stick to a script, following decision trees to answer common questions or handle simple tasks. They sit and wait for input, then respond according to the rules they’ve been given.
• Virtual assistants use natural language processing to handle a wider range of commands, but they’re still reactive: They wait for you to ask.
• AI agents typically bring models, tools, and orchestration together to plan and carry out multi-step actions toward a goal, with a lot more autonomy along the way.

Here’s a way to see the difference: A chatbot can tell you the status of your order. A virtual assistant can place a new order when you tell it to. An AI agent can look at your sales trends, notice you’re running low on a fast-moving item, weigh that against your supplier’s lead time, and decide on its own how much to reorder and when.

Types of AI agents

Not all agents work the same way, and it’s worth knowing the different kinds before you start building. The easiest way to tell them apart is by how they make decisions and take action.

• Simple reflex agents follow basic if-this-then-that rules. A specific trigger leads straight to a predefined action, with no memory and no forethought. A moderation bot that flags a post the moment it spots certain trigger words is a good example.
• Learning agents get better with practice, taking feedback from their environment or from users and adjusting their behavior to get better outcomes. A lead-scoring agent that refines its criteria based on prospects who actually convert is a learning agent.
• Goal-based agents get a specific objective and weigh different paths to reach it, thinking through what each move would lead to. A scheduling agent that finds the best meeting time across different calendars and time zones is a goal-based agent.
• Utility-based agents work a lot like goal-based agents but instead of just finding a path, they weigh trade-offs to decide on the best one. A procurement agent that compares cost, delivery time, and the reliability of vendors when it places orders is utility-based.
• Hierarchical agents work as a team, with a top-level agent breaking a complex goal into smaller tasks before handing them off to specialized sub-agents. A customer service system where the top agent triages requests and routes them to agents that specialize in things like billing or tech support works the same way.

When should you build an AI agent?

Not every problem needs an agent, and knowing which tool fits the job will save you real time.

Choose a chatbot for scripted conversations. If you need to answer common questions with predefined answers, a chatbot is efficient and straightforward. It’s meant for going back-and-forth with a person, even when every answer is scripted in advance.

A few things chatbots handle well:

• Answering questions about store hours, return policies, or shipping windows
• Walking users through basic account or plan questions
• Telling a job applicant where to submit their resume

Use workflow automation for linear tasks. If a process follows the same steps every time, with no conversation or decisions along the way, simple automation handles it well. It just moves data from one system to the next without anyone typing a question.

Workflow automation shines for tasks like:

• Adding a form submission to a spreadsheet and sending a confirmation email
• Moving a new signup onto your email list
• Sending a confirmation email when a job application comes in

Build an AI agent for complex, dynamic goals. If your task requires decision-making, planning, and adapting to new information, an agent is the right choice. Agents work toward a goal and figure out the steps along the way rather than following a fixed script.

This is where AI agents fit best:

• Deciding when and how much inventory to reorder based on sales trends
• Qualifying and scoring inbound leads as they come in
• Screening job applications, updating your ATS, and sending a personalized follow-up

There are times an agent is the wrong call. If you need guaranteed, repeatable output, like a tax calculation or a compliance check, any variation in judgment is a real risk. And if it’s a one-off task you’ll only do a couple of times, the setup probably costs more time than it saves.

What goes into an AI agent

A handful of core components come together to build every AI agent. Here’s what each one does.

• The model powers your agent’s reasoning and language understanding, using an LLM like OpenAI’s GPT or Anthropic’s Claude.
• Tools are the specific actions the agent can perform, like querying a database, sending an email, calling an external API, or handing a task to another AI model. You decide which ones it has access to; the more it has, the more it can do.
• Memory lets an agent retain information across conversations and tasks, so it’s not starting from zero with every interaction. On Bubble, the built-in database handles this automatically. If you’re working with a large document library, you can connect an external vector-database service for semantic search.
• Orchestration is the logic that ties everything together. It takes the goal and uses the model to build a plan, then calls the right tools in the right order. On Bubble, you build this orchestration using visual workflows, so you always have full visibility into how your agent makes decisions.

Common AI agent use cases and examples

AI agents can slot into almost any business process that involves decision-making and a series of actions. Here are a few you’ll see often.

• Automated customer support: An agent can triage incoming tickets by urgency and topic and pull the right answer from your knowledge base, then respond or route it to a person. My AskAI, for example, is a customer support agent built entirely on Bubble with 40,000+ registered users, integrated with tools like Zendesk and Intercom.
• Intelligent lead qualification: An agent can chat with website visitors and ask qualifying questions, then update your CRM with their info and score as the conversation unfolds. Because it adjusts based on what they say, it feels less like a form and more like a real conversation.
• Proactive data monitoring: An agent can watch data streams, like sales figures or website analytics, and spot trends or anomalies, then alert the right team automatically. There’s no rule to write for every scenario: It figures out what’s worth flagging on its own.
• Personalized onboarding: An agent can walk new users through account setup, adjusting the steps based on their answers along the way. If someone gets stuck or drops off, it can flag them for a human to follow up.

How to choose your AI agent platform

Your platform choice affects how fast you can build and how much control you have, and it shapes whether the whole thing scales later. The real question is whether the platform actually builds your app for you, or whether it just generates code that you, or a developer, still have to finish. Tools like Cursor, Lovable, and Replit fall into that second category: They write code fast, but someone still has to read and debug it before it’s production-ready.

Here’s how the main approaches compare.

How to build an AI agent in 7 steps

The steps below walk you through building an AI agent on Bubble, though the principles apply to any platform you choose.

Bubble lets you chat with AI for speed and edit visually for control. Bubble AI generates a working foundation of your app, including pages, workflows, data, and logic. The Bubble AI Agent (beta) is then available in the editor across web and native mobile to help you build and troubleshoot, and it can even resolve some flagged issues automatically.

The Bubble AI Agent’s capabilities are expanding quickly, so check the Bubble AI Agent manual page for the latest on what it can build. Whatever the Bubble AI Agent can’t do (yet), you’ll be able to build with drag-and-drop in the visual editor, which will never generate code you have to read or maintain.

Step 1: Plan what you want your AI agent to do

Start by getting clear on what your agent should actually do. What goal is it working toward, and what tasks will it handle on its own?

You can sketch out your own feature list and requirements, or use Bubble AI to generate a blueprint for you instead. Describe what you want your agent to do in plain language. The more specific you are, the better the output.

For example: “I want to build an AI agent that monitors incoming support emails, summarizes each one, creates a support ticket in our database, and routes it to the correct department based on the message content.”

Bubble AI turns your prompt into that blueprint, outlining the features and tasks it thinks your agent needs. Add or remove specific features directly in the blueprint until it matches what you have in mind, then click Generate to build your app.

Step 2: Design your AI agent’s interface

Most AI agents need some kind of interface, even if they mostly run in the background. That might be as minimal as a button to trigger an action or a status page that logs what the agent has done. If your agent interacts directly with users, you’ll want something more developed.

Bubble gives you a few ways to approach this:

1. Generate it with Bubble AI. If you created a blueprint in Step 1, Bubble AI can turn it into a complete interface based on your defined features.
2. Design it yourself. Use Bubble’s visual editor for full control over layout and components.
3. Start from a template. Customize a prebuilt template from the Bubble Marketplace to fit your use case.

Once Bubble AI generates your interface, you can customize every part of it in the editor. The AI never locks you in.

Step 3: Connect to AI models and other tools

Your agent needs a connection to the AI model that powers its reasoning, like OpenAI’s GPT or Anthropic’s Claude, plus any other systems it works with. Different LLMs have different strengths, so you can connect to multiple models. You might use one for summarization and another for planning or decision-making.

There are two ways to set this up on Bubble:

Configure it yourself if you want full control over exactly how the connection works. The API Connector is a dedicated tab in the visual editor that lets you connect to AI services and other JSON-based REST APIs; it supports cURL import, structured response inspection, and streaming for responses that arrive gradually. Once you test and initialize a call, Bubble parses the JSON response into structured data you can use in dynamic expressions and workflows.

Let the Bubble AI Agent set it up if you’d rather skip manual configuration. Describe the service you want to connect, and the Bubble AI Agent can set up the API Connector call and write the workflow that uses it, or search for, suggest, install, and uninstall plugins when a ready-made integration already exists.

Your agent will likely need other tools too, like a CRM, email platform, or Slack. Connect these the same way, through the API Connector, a plugin, or the Bubble AI Agent.

Step 4: Set up your AI agent’s memory with Bubble’s database

Memory gives your agent context across conversations and tasks, not just within a single one. It enables:

• Multi-step conversations with context: The agent remembers what was said earlier and builds on it, rather than treating each message as a fresh start.
• Task handoff between agents or users: When work passes from one agent to another, or from an agent to a person, the relevant context travels with it.
• Dynamic personalization: The agent adapts based on each user’s past interactions, preferences, and history.

On Bubble, you can use the built-in database as structured, persistent memory for conversation history and user preferences, without needing to know SQL. You set up custom data types and apply privacy rules to control access, then pull the right data back out with built-in search tools when you need it.

If your agent needs to search through large amounts of unstructured text, like a knowledge base of help articles or thousands of past support tickets, a regular database search may not be precise enough. For that, connect Bubble to an external vector database through APIs or compatible plugins. It indexes your documents by meaning, so your agent finds what’s relevant instead of just matching exact words.

Step 5: Build workflows to power your agent’s logic

Workflows are what carry out your agent’s decisions. Once your agent figures out what to do, whether that’s replying to a message or updating a CRM record, a workflow is what actually makes it happen.

Your workflows will coordinate how and when your agent:

• Interprets user input or incoming data
• Chooses a course of action based on current context
• Calls AI models, APIs, or internal tools
• Updates the database
• Notifies users, sends emails, or triggers other workflows

On Bubble, a workflow defines which actions run in response to a triggering event, like a user clicking a button, a condition being met, or a scheduled event firing. For example, you might create a Bubble workflow that listens for a user request, sends it to an AI model for interpretation, then runs different follow-up actions depending on the model’s response.

There are two ways to build one:

Build it yourself in the workflow editor, chaining together the events, conditions, and actions until the logic works exactly the way you want.

Let the Bubble AI Agent build it by describing what you want. It can generate and modify supported frontend and backend workflows, including scheduled and API workflows and common data or API Connector actions. Its capabilities here are expanding quickly, so check the manual for the current list of what still needs manual setup.

You can chain workflows together or set them up on a schedule so your agent runs independently. Because Bubble shows your app logic in visual workflows rather than code, you can understand what was built, make precise changes yourself, and keep going when AI reaches its limits.

Step 6: Test and refine your agent’s behavior

Once you have the core pieces in place — interface, integrations, memory, and workflows — it’s time to test how your agent actually behaves. Run it through different scenarios and pay attention to where it does the right thing and where it doesn’t.

You can also give the Bubble AI Agent access to your app data in settings, so it can inspect development-database records to help debug data-driven behavior. This still respects your privacy rules and any sensitive-field protections you’ve set up.

Things to watch for:

• How well your agent interprets different kinds of input
• Whether it takes the correct actions across different scenarios
• How memory and context affect behavior over time
• How it handles edge cases and unexpected inputs

Start with simple tests, then work up to more complex scenarios with multiple steps, unexpected inputs, or handoffs between workflows. Refine your prompts and logic until the behavior is consistent.

Step 7: Deploy and iterate

Once your agent is behaving reliably, you’re ready to launch, and Bubble lets you deploy with just a few clicks.

From there, keep iterating. Gather feedback, watch how your agent handles real users and real scenarios, and adjust as you learn.

Bubble carries you the whole way, from that first AI-generated draft to real users in production. That includes version control, logs and debugging tools, Security Dashboard checks (depth depends on your plan), built-in hosting, and infrastructure that scales for both web and native mobile.

Start building your AI agent today

You don’t need an engineering background to build the AI agent you need. On Bubble, you can create anything from a simple automator to a multi-step agent running complex business workflows.

Start with AI, then refine and ship when it’s ready. You’ll always see exactly how it works and can adjust it yourself anytime, without ever generating code.

Start building with a free account →

Frequently asked questions about building AI agents

Can anybody create an AI agent without coding experience?

Yes, Bubble’s visual platform lets you design interfaces and set up databases without writing code, then build your agent’s logic through workflows. You can see how everything connects and modify your agent’s behavior directly rather than prompting and hoping for the right result.

Can you build an AI agent with ChatGPT?

You can use ChatGPT and other coding agents to generate code for an AI agent, but turning that code into a reliable production app usually requires someone to review, debug, test, deploy, and maintain it. If you want AI speed without getting stuck maintaining generated code, Bubble lets you vibe code without the code: build on one platform with infrastructure, database, privacy rules, workflows, and deployment built in.

Is it free to build an AI agent?

You can start building and testing on Bubble’s free plan. Paid plans are available when you’re ready to launch, so check Bubble’s pricing page for the current plan tiers and billing options.

You’ll also need to budget for API usage costs from your chosen AI model, since providers like OpenAI publish token-based pricing that varies by provider and model.

How long does it take to build an AI agent?

Build time depends on the agent’s scope, integrations, data model, testing needs, and production requirements. Bubble AI can speed up the initial foundation, while complex multi-system agents usually require more planning, setup, and iteration.

What is the hardest part of building an AI agent?

One of the hardest parts is capturing the implicit knowledge your team uses to get work done: the judgment calls and edge cases that aren’t written down anywhere. Building an agent forces you to make that knowledge explicit by defining the rules and decision criteria a human would handle intuitively.