AI agents have advanced rapidly in recent years, making it easier than ever for anyone with a computer to create one of their own. As one of the fastest-growing trends in artificial intelligence, agents are expected to see widespread adoption across multiple industries.
Whether your goal is to streamline workflows or design a personalized assistant, we walk you through the key steps to developing an AI agent using leading platforms and practical, tested approaches.
Before you do anything else, you have to first know the purpose of your AI agent. You have to begin by defining what use case it will serve for you or your organisation. Determining it's purpose will allow you know what tool or services, workflow, and also platform that will be necessary.
There are different popular AI agent use cases and below are a few of them.
- Sales-focused AI agents assist customers by clarifying product details, suggesting alternatives, comparing features, and providing up-to-date pricing. They can also qualify potential buyers and guide them smoothly through different stages of the sales pipeline.
- Customer service AI agents handle support needs by resolving complaints, sharing helpful resources such as tutorials or FAQs, and diagnosing customer problems.
- Knowledge management AI agents make it easy for employees to access company policies, pull key points from long documents, and quickly locate information stored across internal systems and knowledge bases.
- AI lead generation agents nurture prospects by sending personalized follow-up messages across channels like email or Telegram, collecting details during conversations, and automatically updating CRM records to improve lead tracking.
- HR AI agents support staff by answering questions about workplace policies, simplifying the onboarding process, and automating leave or time-off requests.
- E-commerce AI agents enhance shopping experiences by tracking orders, checking product stock, and suggesting personalized product recommendations based on user history and preferences.
If you have a specialized industry, you can build AI agents that tackle 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 AI platform, the world is your oyster. A well-designed AI agent can automate nearly any task.
There are few good AI agent platforms to choose from. Selecting the right platform is crucial for your success.
There are different factors to consider. Make sure you pick an AI platform that:
- Provides learning materials – Since there’s always a learning curve, choose a platform that supplies clear guides, tutorials, and documentation to help you get up to speed.
- Aligns with your goals – Select a platform designed for your use case. For example, if you need a sales assistant or multi-agent setup, don’t settle for one that only focuses on customer support. Make sure it matches your specific AI needs.
- Offers flexible pricing – A low cost option lets you explore the platform, test features, and experiment with building agents before investing huge amounts.
Today’s AI platforms give users multiple paths to create autonomous agents:
- No-code tools let you build quickly with drag-and-drop interfaces and minimal technical expertise.
- Low-code frameworks strike a balance between simplicity and customization, giving you more room to adapt features.
- Custom development is ideal for complex projects, offering full control over how your agent operates.
- Open-source options are also widely available, providing flexibility and community-driven innovation for those who want greater transparency.
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.
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.
The best platforms allow users to build AI agents that decide when to use a structured flow and when to use an LLM. Developers simply need to prompt the autonomous system in plain language.
In a few lines of simple text, you can tell your AI agent what you would like it 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 agent 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 for more flexible, intelligent responses.
This hybrid approach creates flexible agents that can both follow scripts and handle complex, open-ended tasks that require reasoning and creativity.
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 multiple variables that you include in order to collect information systematically.
An AI agent's true power comes from its tool integrations. These integrations are what allow an AI agent to seamlessly integrate with existing workflows, rather than being an 'extra' with no connectors.
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 include:
- Internal documentation
- Product databases
- Compliance repositories
- Enterprise search systems
The strongest systems use retrieval-augmented generation (RAG) to parse through documents and retrieve relevant information. RAG comes built-in with quality AI agent platforms.
Channels are simply the mediums through which people interact with your AI agent. For example, a WhatsApp bot connects with users directly on WhatsApp, while a Discord bot engages within Discord communities.
One of the most common setups for customer-facing agents is a website chat widget (often called webchat), which lets visitors ask questions or get support right from your site.
But an AI agent isn’t restricted to just one channel. You can design it to collect inquiries from Facebook Messenger and send notifications on Slack, or even broadcast messages across multiple platforms like Telegram, SMS, and email simultaneously.
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 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
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 test 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. 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. Be prepared: this process will continue even after you deploy your AI agent. It's completely normal and part of building robust agentic AI.
When your AI agent is built and ready to go, the next step is launching it so it can start delivering value. Make sure your audience knows it’s available—an unnoticed agent can’t serve its purpose. Announcing its launch clearly and guiding users on how to access it ensures the agent becomes a useful and trusted resource.
If you’re developing a multi-agent system—where several AI agents operate within the same environment—you’ll need to design a strategy for agent routing, which ensures that the right triggers are sent to the right agents.
To evaluate how effectively these agents work together toward shared objectives, a multi-agent evaluation framework is essential. This helps you account for the additional complexity that comes with coordinating multiple autonomous agents in collaboration.
Launching your AI agent is only the first step—its real growth comes from listening to your users. Collecting and analyzing user feedback is critical to understanding how well the agent is serving its purpose.
Feedback helps reveal where the agent excels and where it falls short, guiding improvements in areas like response accuracy, ease of use, and overall satisfaction. By regularly acting on what users share, you can continuously refine the experience and ensure your AI agent becomes more reliable and valuable over time.
Creating AI agents is easier today than ever before. The first step is setting clear objectives so your agent’s role and capabilities are well-defined from the start. Next, select a platform that fits your specific use case, provides strong support resources, and gives you the freedom to experiment before committing.
A winning approach combines structured workflows with LLM-powered reasoning, enabling agents to both follow predictable scripts and handle more open-ended, complex tasks. By connecting your agent to knowledge bases, communication channels, webhooks, and enterprise services, you can embed it directly into your daily operations.
Once built, your agent should be thoroughly tested, deployed, and continuously refined through analytics and user feedback. This ongoing improvement ensures your AI solution grows smarter and more valuable over time.
The future of automation belongs to agentic AI systems—agents that can think, reason, and act independently while integrating seamlessly into existing business infrastructure. That’s where Skynet AI Agent Studio stands out: it provides the flexibility, scalability, and tools you need to design and launch agents that truly transform customer service, sales processes, and complex workflows.
If your goal is to unlock intelligent automation and stay ahead in the age of AI, Skynet AI Agent Studio is the platform to start with.