The ultimate goal of an AI agent is to create a type of artificial intelligence that completes various complex tasks similar to humans. The AI agents perform activities like we are, responding to events and make decisions. The existing AI agent is the large language model ChatGPT, which can handle knowledge-based questions. It can receive instructions and perform reasoning based on the knowledge that it learned through training. If we ignore physical efforts, this model is somewhat similar to human thinking, but still incapable of many tasks that require higher cognitive abilities.
We humans remember information for a long time, but ChatGPT does not. In addition, we also have the continuous memory ability; when we are in a situation where we face a huge challenge, we normally decompose problems into specific steps and complete them step by step. Sometimes we’re utilizing various methods and functions from the outside world. But concerning ChatGPT, we do not have continuous memory and are unable to maintain the cognition state for a long period.
The AI agent can fulfill the human dreams of humans when it has the following philosophical core:
- Cognitive: perception, reasoning, action
- Goal-oriented: Agents have the power for optimal decision-making based on value function.
- Autonomous: Perform learning
Currently, the initial capabilities of AI agents are based on large language models and have some limitations, such as insufficient memory capacity. If you look toward the sub-agents based on ChatGPT, you can find that all of them are trying to provide short-term and long-term memory capability to their agents. For example, when you open an agent application, you always find the past activities.
Today, people want to create AI agents that can use external tools like the Internet to obtain real-time information. For example, weather bots or ordering takeout. They want to create AI agents that further expand the application scenarios, truly connect with the real world, and meet the actual needs of users.
Industry current agents
- To handle the common consulting questions.
- Buy and sell the tickets.
- Automatic order generation
- Recommend travel routes
Meanwhile, those AI agents are considered successful, which are able to design intelligent navigation and recommendation plans. Currently, our digital world needs this capability to improve user experience. For example, image generation scenarios where MidJourney and Stable Diffusion generate images through natural language processing. This will show us the expansion into the application field of AI agents and bring new possibilities.
Initially, the AI agent capability is based on knowledge, and you can utilize this feature to create industry-specific solutions like scenic spot scenarios or knowledge of scenic spots. During the business routines, this information is crucial for business.
In summary, if you are interested in developing an AI agent for commercial purposes, you can take advantage of the shortcomings of large language models in private domain-specific knowledge. If you can provide the essential functions for business with the help of AI agents, you can improve business efficiencies.
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