How to Build an AI Agent

TL;DR: AI agents differ from chatbots and virtual assistants because they make decisions autonomously, plan multi-step actions, and work toward goals without needing a specific command for every step. They’re built from four core components: an LLM, tools, memory, and orchestration logic. You can build and deploy one without coding by defining your agent’s goal, connecting AI models and external tools via APIs, storing context in a database, and coordinating behavior through visual workflows.

AI agents handle complex tasks, make decisions on the fly, and operate autonomously. If you’ve been wondering whether you can actually build one yourself, especially without a technical background, the answer is yes.

Bubble is built for exactly this. You chat with AI to generate your agent’s foundation, then edit everything visually so you stay in control. No code required at any step.

Understanding AI agents

An AI agent is an autonomous system that makes decisions and takes action to achieve specific goals. Unlike traditional apps that follow predefined instructions, agents analyze new situations, plan multi-step responses, and act without needing a specific command for every step. They combine a large language model (LLM) for reasoning, tools like APIs and databases for taking action, memory for retaining context across interactions, and orchestration logic that coordinates it all.

To make that concrete: Think about keeping CRM records up to date. A traditional software solution needs explicit rules for every type of input and output it might process. An AI agent can review emails and Slack threads, identify when a deal has moved to a new stage, extract the relevant details, update the CRM, and notify the right team.

Agents use LLMs to interpret natural language and reason through goals. They might send an email, query a database, summarize a document, or trigger a set of actions based on what a user asked for. Some run entirely in the background, while others interact directly with users through a chat interface or embedded app component. Some handle narrow, specialized tasks, and others are more flexible, chaining actions together and calling multiple tools.

What makes AI agents different from chatbots and virtual assistants

AI agents are often confused with chatbots or virtual assistants, but the differences are meaningful. All three use AI to interact with users, but their capabilities are quite different.

• Chatbots: These follow predefined scripts and decision trees to answer common questions or handle simple tasks. They wait for input and respond according to rules.
• Virtual assistants: These use natural language processing to understand a wider range of commands. They’re still reactive, though, waiting for a user prompt before taking action.
• AI agents: These are proactive and autonomous. They make decisions, plan multi-step actions, and work toward goals without needing a specific command for every action.

Here’s a simple way to see the difference: A chatbot can tell you the status of your order. A virtual assistant can place a new order for you. An AI agent can monitor your inventory, predict when you’ll run out of an item, and place a new order automatically.

When should you build an AI agent?

Not every problem needs an agent. Knowing which tool fits the job will save you a lot of time. The key question is: Does your task require autonomous decision-making, or does it follow a predictable path?

• Choose a chatbot for scripted conversations. If you need to answer common questions with predefined answers, like store hours, return policies, or pricing tiers, a chatbot is efficient and straightforward. The interaction follows a predictable path and there’s no need for the system to reason through novel situations.
• Use workflow automation for linear tasks. If a process follows the same rule-based steps every time (for example, when a form is submitted, add the data to a spreadsheet and send a confirmation email), simple automation handles it well. No decision-making required.
• 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.

A good example of when an agent earns its place: A chatbot can tell a job applicant where to submit their resume. A workflow automation can send a confirmation email when the application is received. An AI agent can review the application, score it against the job criteria, update the ATS with the candidate’s status, and draft a personalized follow-up, all without anyone touching it.

What goes into an AI agent

AI agents are built from a few core components that work together. Understanding what they are makes the build process much less intimidating.

• The model: This is the LLM, such as current OpenAI GPT models or Anthropic’s Claude models, that powers the agent’s reasoning and language understanding. It interprets requests, makes plans, and decides what to do next. Different models have different strengths: some are better at reasoning through complex problems, others at following precise instructions.
• Tools: These are the specific actions the agent can perform, from searching a database and sending emails to calling an external API or passing a task to another AI model. The more tools an agent has access to, the more it can do.
• Memory: This is how an agent retains information across conversations and tasks. Without memory, every interaction starts from zero. On Bubble, the built-in database gives your agent a persistent, structured way to store and retrieve context, user preferences, and past actions. Specialized retrieval use cases can be handled through external vector-database integrations.
• Orchestration: This is the logic that ties everything together, taking the goal, using the model to create a plan, and calling the right tools in the right order. In 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 be applied to almost any business process that involves decision-making and a series of actions. Here are a few common ones.

• Automated customer support: An agent can analyze incoming support tickets, categorize them by urgency and topic, pull relevant information from a knowledge base, and either respond or route the ticket to the right person. It adapts to each ticket rather than following a fixed script.
• Intelligent lead qualification: An AI agent can engage with potential customers on your website, ask qualifying questions in a natural, conversational way, and update your CRM with the lead’s information and score. It adjusts its questions based on what the prospect says, so the interaction feels less like a form and more like a conversation.
• Proactive data monitoring: An agent can watch data streams from sources like sales data, website analytics, or social mentions, spot trends or anomalies, and automatically generate a summary report or send an alert to the right team. You don’t need to set up a rule for every scenario. The agent decides what’s worth flagging.
• Personalized scheduling: An agent can manage your calendar by coordinating with others over email to find a meeting time that works, factoring in your preferences and existing schedule, and sending the invite. It takes care of the back-and-forth so you don’t have to.

How to build an AI agent in 8 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 directly in a visual editor for control, so you can understand what you’re building at every layer. Bubble AI generates a working foundation, including pages, UI, workflows, database schema, and app logic, that you can refine visually. The Bubble AI Agent (beta) helps you add supported features, troubleshoot issues, and learn Bubble as you build.

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? What tasks will it handle on its own? Will it respond to user input, act on backend data, or monitor for specific events?

You can think this through manually, or use Bubble AI to generate a blueprint. 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 will turn that into a structured blueprint with a breakdown of the features your agent will need. Keep prompting to add, remove, or refine until the blueprint reflects exactly what you have in mind.

Step 2: Choose the right AI agent platform

Your platform choice affects how fast you can build, how much control you have, and whether you can scale. Here’s how the main approaches compare.

Step 3: 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.

Whatever you generate with AI can be previewed and customized in Bubble’s editor. You’re never locked into what the AI produces.

Step 4: Connect to AI models and other tools

Your agent needs access to the systems it will work with. Start with the AI model that powers its intelligence. Bubble lets you connect to models like OpenAI’s GPT and Anthropic’s Claude through the API Connector, which is now a dedicated Editor tab with easier API collection navigation, cURL import, response inspection, and streaming support. You can also connect through plugins and external APIs.

Connecting to multiple models is possible too. You might use one for summarization and another for planning or decision-making.

Beyond the AI model, set up integrations with the other tools your agent will use. Depending on what it’s doing, that might include a CRM, email platform, Slack, or any external API your workflows need to call.

Step 5: 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 records, user preferences, and past actions. No SQL required. You define custom data types, apply privacy rules to control access, and use search tools to retrieve the right data when needed.

For semantic memory or retrieval across large document sets, external vector databases can be integrated to handle that kind of workload.

Step 6: Build workflows to power your agent’s logic

Workflows in Bubble define which actions run in response to a triggering event, like a user clicking a button, a condition being met, or a scheduled event firing. You can build workflows visually in the workflow editor. The Agent can help generate and modify supported frontend workflows, though backend workflows, plugin actions, payment actions, custom events, and more complex logic will need to be built manually.

For AI agents, workflows handle the full chain: receiving input, processing it, storing memory, and triggering actions. Whether your agent is replying to a message or updating a CRM record, that behavior is defined in your visual workflows.

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

A typical pattern: A workflow listens for a user request, sends it to an AI model for interpretation, then runs different follow-up actions depending on what the model returns. 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 7: 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.

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 8: Deploy and iterate

When your agent is behaving reliably, you’re ready to launch. 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 gives you version control, logs, issue-checking tools, a security dashboard, built-in hosting, and infrastructure designed to scale with your app.

Start building your AI agent today

You don’t need an engineering background to build something powerful. Define your goal, connect your tools, give your agent memory, and wire up the logic — on Bubble, all of that is visual, so you can see exactly how your agent works and adjust it yourself at any point.

Whether you’re building a simple automator or a multi-step agent that handles real business workflows, the same platform gets you there. Start with AI to generate a foundation, refine it in the visual editor, and ship when it’s ready.

Start building with a free account →

Frequently asked questions about building AI agents

What’s the difference between AI agents and chatbots?

The main difference is autonomy. Chatbots are reactive and follow scripts to answer questions. AI agents are proactive; they make decisions, perform multi-step tasks, and work toward a goal without needing a specific command for every action.

How long does it take to build an AI agent?

Simple agents take hours or days on Bubble, while complex multi-system agents may take several weeks — significantly faster than traditional development, which can take months.

How much does it cost to build an AI agent?

Bubble has a free plan for building and testing, with paid plans starting at $29/month (web, billed annually) for launching to production. Check the pricing page for the full plan breakdown. The other main cost is API usage for the AI model. OpenAI and Anthropic both charge per million tokens processed, with separate rates for input and output.

What is the hardest part of building an AI agent?

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

Do I need coding experience to build an AI agent?

No. Bubble’s visual platform lets you design interfaces, set up databases, and build your agent’s logic through workflows without writing code. You can see how everything connects and modify your agent’s behavior directly rather than prompting and hoping for the right result.