Ecosystem vision

Every website, e-commerce store, content creator, blog—anything on the internet—will have its personal AI agent.

It’s just too practical and efficient. Let’s take an example: an e-commerce store. Physical stores have assistants because people always have questions and problems. How businesses manage these issues can make or break them.

Doing this efficiently costs a lot of human resources, which means money and shrinking profits. So how do AI agents come into play in this scenario? Customer support and store management. If you buy something online and need information, you have two options: search online (Google it) or contact customer support if something is wrong.

In a physical shop, you can ask an assistant, and they solve your problems very quickly. So, how does an AI agent come into play here?

1. 24/7 Customer Support: AI agents can provide instant support around the clock, answering common questions and resolving issues without the need for human intervention.

2. Personalized Assistance: AI agents can learn from customer interactions and provide personalized recommendations and solutions, enhancing the shopping experience.

3. Efficiency and Cost Savings: By handling routine inquiries and problems, AI agents reduce the need for a large customer support team, saving on labor costs and improving profit margins.

4. Consistency: AI agents provide consistent support and information, ensuring customers receive accurate responses every time. In essence, AI agents can transform customer support and store management, making them more efficient, cost-effective, and customer-friendly.

AI Agent - The agent has a set of commands to help customers navigate the shop, find the right products, give suggestions, and answer customer questions.

It uses all available information about products and company policies. After completing this process, it records the interaction and all the steps it took. This data is collected and saved for further steps.

Data - The collected data is saved, analyzed, and compared against the initial goals. AI analysis algorithms will provide insights on what could be improved.

Model Training - Insights from data, combined with additional human input correcting and highlighting extra points, lead to model training cycles. This results in a new set of actions designed to achieve better results.

Personal AI - This three-step process is the foundation of personal AI, the brains behind your agent. This cannot be achieved by general-purpose LLMs like ChatGPT or Gemini. It requires refinement through these cycles to learn, improve, and become a custom personal AI that your AI agent can run.

The most beautiful part is that this approach can be applied to almost anything on the internet. There will be different types of AI agents for different tasks, purposes, and user experience levels, just like with website builders.