AI agent technology has made incredible strides in recent years – and that means today, building your own AI agent is accessible to anyone with a computer.
AI agents are one of the leading AI trends, projected to continue their rapid adoption across industries.
Whether you’re automating processes or creating an AI assistant, this guide will take you through the steps to build your own LLM-powered AI agent.
1. Define Your Scope
The first step to create an AI agent is simple – what’s it going to do? Start by clearly outlining the purpose of your agent.
There are plenty of real-world applications of AI agents. Identifying the purpose of yours will determine what capabilities it will need, which will determine the platform you use.
- A sales AI agent helps users by answering product questions, recommending options, comparing models, and providing pricing details.
- A customer support AI agent resolves customer issues, shares resources like FAQs or videos, and troubleshoots technical problems.
- A knowledge management AI agent retrieves company policies, summarizes documents, and helps employees quickly find relevant information.
- An AI lead generation agent sends targeted follow-ups via email or platforms like WhatsApp, captures information through conversations, and syncs data with CRMs for streamlined tracking.
- An HR AI agent answers employee queries about company policies, assists with onboarding, and handles PTO requests.
- An e-commerce AI agent tracks orders, checks product availability, and offers tailored recommendations based on user preferences.
If you have a specialized industry, you can even build an AI agent that tackles multiple processes. For example, an AI agent for real estate can suggest properties, keep track of paperwork, and manage client relationships. Or an AI agent for hotels can handle bookings, streamline housekeeping requests, and sell extra services.
If you use an extensible platform, the world is your oyster. A well-designed AI agent can automate nearly any task.
Once you’ve settled on your scope, you have the information you need to pick a platform.
2. Pick a Platform
There’s no shortage of AI agent frameworks to choose from. If you’re looking for inspiration, our curated list of the top 9 AI platforms is a great place to start.
While I won’t compare platforms here – because, admittedly, I’m partial to ours – I can share a few key factors to consider when selecting the right platform for your project:
Make sure you pick an AI platform that:
- Offers educational resources. There’s always going to be a learning curve, so ensure you’re well-equipped for it.
- Matches your intent. Don’t pick a platform that specializes in customer service if you want a sales bot or a multi-agent system.
- Includes a free tier, so you can test it out before (or without) making a financial commitment.
If you need an open-source solution, there are plenty of open-source AI agent options to choose from, too.
Once you pick your AI agent builder to start off with, you can start building your own AI agent.
3. Create Instructions and Variables
Your AI agent is going to be entirely unique – it depends entirely on your use case and scope. Part of the process will involve familiarizing yourself with your platform of choice and applying your understanding to your unique roadmap.
Start with an Autonomous Node
Let’s highlight an unfortunate truth: not all ‘AI agent platforms’ will allow you to build real AI agents.
Many of them offer AI chatbots, but lack a key component of AI agents: the ability for an agent to make decisions on its own to fulfill the builder’s request.
In the Botpress Studio, Autonomous Nodes allow users to build AI agents that decide when to use a structured flow and when to use an LLM. Devs simply need to prompt the Autonomous Node in plain language.
In a few lines of simple text, you can tell your Autonomous Node what you would like your AI agent to do and how it should act while doing it. You can define its personality, scope, and purpose in minutes.
Some parts of your AI chatbot should be structured – like your greeting or your targeted sales pitch. But chances are that there will be some aspects of the conversation that you want to offload to an LLM.
Create variables to collect information
Your AI agent will have some questions for your users. For example:
- A travel AI agent might ask what city the user wants an itinerary for
- A mental wellness AI agent might ask how a user is feeling
- A customer service agent will ask what a user needs help with
Depending on your conversation flow, there will be 1-x variables that you include in order to collect information.
For example, a travel AI agent might ask where the user is going, if they’re looking to book a flight, how many people they’re traveling with, their budget, their preferred activities, etc.
Or a sales agent might ask what a user is looking for, and then dive into different conversation flows based on their answer.
4. Integrate Your AI Agent
An AI agent without integrations is just your own version of ChatGPT. An AI agent's purpose is defined by its integrations.
There are many entities you can integrate with an AI agent — nearly infinite options if you use a flexible platform.
These integrations are what allow an AI agent to seamlessly integrate with existing workflows, rather than being an 'extra' with no connectors.
Knowledge Bases
If you want your agent to 'know' any bespoke information — like product availability, local bylaws, or software documentation — you'll often share this information through a Knowledge Base.
Using a Knowledge Base allows your AI agent to communicate accurate and up-to-date information (unlike asking a general purpose chatbot like ChatGPT).
A Knowledge Base can be anything from a table or a document to a full-blown database. Examples of KBs include internal documentation, product databases, compliance repositories, or enterprise search systems.
The strongest systems will use retrieval-augmented generation (RAG) to parse through documents and retrieve relevant information. (Don't worry, RAG will come with an AI agent platform.)
Channels
Channels are how your users can communicate with your AI agent. They're pretty self-explanatory: a WhatsApp chatbot communicates through WhatsApp. A Discord bot communicates on Discord.
A common channel for customer-facing AI agents is a website widget. Sometimes called webchat, this type of channel allows your website visitors to interact with your agent.
Is an AI agent limited to 1 channel? Definitely not. You can integrate your agent to receive information from Facebook Messenger and then ping you on Slack. Or build an AI agent that sends messages to all your contacts across Telegram, SMS, and email.
Webhooks
If you want your AI agent to take action based on triggers, you'll need webhooks. These kinds of automated event notifications allow AI agents to communicate with different systems in real time.
When an event occurs in one system, the webhook sends a request to another system. This can trigger an action without requiring human input. Examples of using webhooks include:
- A new lead in Salesforce prompts the AI agent to score and assign it.
- Customer support tickets trigger AI agents to categorize and escalate as needed.
- AI agents send shipping updates when an order status changes.
- New employees get training materials and meeting invites from the AI agent.
- Security alerts prompt the AI agent to analyze and notify IT teams.
Platforms
The most difficult, most exciting, and most useful of AI agent integrations: platforms.
Don't let the difficultly dissuade you — most platforms will come with a host of pre-built integrations for AI agents.
Examples of platforms you can integrate with an AI agent include:
- CRM platforms like Hubspot and Salesforce, for tracking and nurturing leads
- Helpdesk platforms like Zendesk and Intercom, for customer support and ticket resolution
- Marketing automation tools, like Mailchimp (or Hubspot again) for sending external emails
- ERP systems, like Oracle or SAP, for streamlining inventory management
- Analytics platforms like Google Analytics, for measuring agent outcomes
For example, an AI agent for HR will use a company’s key policy documents as its Knowledge Base. When an employee asks how to handle a specific situation, the chatbot can use the policy documents to inform its answer.
5. Test and Iterate
After building your AI agent, the next step is refining it. Testing and iteration are essential for success but are often overlooked by builders eager to launch.
Your AI agent platform should offer a simulator within its studio, allowing you to practice interactions with your AI agent. This is your first step in testing and a crucial part of fine-tuning your agent during the development process.
Once you’ve finished your initial build, you can share a sample version of your agent with friends or colleagues using a URL. Testing it this way helps ensure its functionality is ready before deployment.
As you test, you’ll be able to tweak your AI agent for the better. And be prepared: this process will continue even after you deploy your AI agent. It’s normal.
6. Deploy Your AI Agent
Once your AI agent is ready, it’s time to deploy it and let it start making an impact. There are several deployment options to choose from:
- Deploy it as a widget on your website.
- Share it with users through a URL.
- Integrate it with messaging channels like WhatsApp, Instagram, Telegram, Facebook Messenger, or Slack.
- Integrate it with bespoke platforms or services, like your company’s internal messaging board or proprietary software.
Don’t forget to let your users know the AI agent is live – if they don’t know it’s available, it can’t fulfill its purpose effectively. Clear communication is key to making your AI agent a valuable resource.
Note: If you're building a multi-agent system — multiple AI agents in a shared environment — then you'll also need to plan for AI agent routing, the process of directing triggers to specific agents.
To measure the success of how well your multi-agent system is collaborating to achieve its goal, you'll need a multi-agent eval system to evaluate it. This will address the added complexity that comes from having multiple agents working together.
7. Monitor and Improve
Your AI agent project doesn’t end after deployment—in fact, deployment is just the beginning. Once it’s out in the world, your AI agent starts working for you.
A quality AI agent platform will offer ongoing analytics, providing insights into when people are using your agent, the topics they’re asking about, and the platforms they prefer to engage with.
If you want to better understand how to optimize your use of analytics for an AI agent, you can check out our article on AI chatbot analytics.
Start Building an AI Agent For Free
You have an idea for an AI agent – and we have the most powerful and flexible AI agent platform.
It’s easy to build on Botpress with a drag-and-drop visual flow builder, extensive educational library, and an active Discord community of 20,000+ bot builders.
Our extensible platform means you can build anything, and our Integration Hub is full of pre-built connectors to the biggest channels.
Start building today. It’s free.
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