Intelligence is our identity. In our world, we humans believe that we are the only species that has intelligence. The emergence of AI intelligent agents also indicates we are hungry for intelligence. Our nature demands more value with less hard work; therefore, everyone rushes to produce artificial intelligence-based agents or intelligent agents for their survival. Many industries or manufacturers show their extreme interest in large models and launch their models one after another. Many platforms help people create knowledge-based agents with artificial intelligence.
What is an AI virtual agent?
These are the software components that are capable of automatically performing tasks such as commenting on the posts. Some industries seek agents that reduce repetitive labor operations, and there are many other versions of AI virtual agents. If you are also interested in developing your native version of AI virtual agents, then you do not jump into the development blindly. If the agent is suitable for your work and reduces the cost of operation and business methods, then you focus on the development of an AI agent.
Of course, if you believe that your AI agent can magnify your potential then consume your resources, do not be confused by some flashy functional demonstrations. First, think about the AI capability that solves your problems, and if you believe you will reach the stage for commercialization based on use case scenarios, then devote your time and money.
Types of AI agents
The capabilities of large AI models are different, but before utilizing their practice routes, focus on your scenario requirements, like what the operating cost is and how you determine the technical route. Currently, many tools are being innovated. So, you need to focus on features that supplement your use case scenario.
If you have no immediate requirement for certain tools and do not feel to solve any problems, then it’s better not to invest your time. It is much better to prioritize learning more important knowledge that does not become obsolete immediately.
Start your AI agent development with scenario problems in mind. If the AI agent improves efficiency, devote your efforts. But before participating in any development work, first answer the following questions.
- What is its operating cost?
- Does the AI agent meet expectations?
- Is the AI agent’s operating speed satisfactory?
Technology stack
Are you familiar with AI programming tools? If not, then devote your time to learning some AI programming. Because when you are developing an AI agent, you need to connect with third-party systems, integrate existing tools, or directly use some open-source projects. Learning AI programming does not mean building the complete project but learning how to pull open-source projects from the source code platform, debug them, modify the API key of a large model, and add your native business code.
If you have any business use case scenarios, then Coze and Dify are enough to create your AI agent, and you also do not require producing your code. This platform does not require putting your coding efforts. If you are capable of going a little deeper, then the AI programming tool, Bolt.DIY, is perfect. In case you are interested in a multi-agent development framework, then CrewAI is enough, and it does not require extraordinary coding skills.
Programming language: Python
front-end: React
Node: Bolt.DIY / Bolt.New
VSCode: Windsurf/Cursor
AI Agent Development Framework: LangGraph and Crew AI
Database: PostgreSQL
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