Through this article, you will learn the complete process of building an AI agent with Llama 3 model function call capabilities based on the open source deep learning model visualization tool Gradio.
Abstract: The multi-AI agent model is a powerful artificial intelligence architecture that uses the collaboration and interaction between multiple agents to solve complex problems, perform diverse tasks, and simulate complex system behaviors. In this model, each agent has independent perception, decision-making, and action capabilities, and optimizes the overall system goals through mutual collaboration and information sharing.
At present, the industry generally believes that applications based on large models are concentrated in two directions: RAG and Agent. Regardless of which application, designing, implementing, and optimizing applications that can fully utilize the potential of large models ( LLM ) requires a lot of effort and expertise. As developers begin to create increasingly complex LLM applications, the development process inevitably becomes more complicated. The potential design space of such a process can be huge and complex. The article " How to Build Apps Based on Large Models " provides an exploratory basic framework for large model application development, which can basically be applied to RAG and Agent. However, is there anything unique about agent-oriented large model application development? Is there a large model application development framework focused on Agent?
Many people may wonder, Agent seems to be not that far from LLM, so why is Agent so popular recently, and why is it not called LLM-Application or other words? This has to start with the origin of Agent, because Agent is a very old term, and can even be traced back to the remarks of Aristotle and Hume. In a philosophical sense, agent refers to an entity with the ability to act, and the word agent refers to the exercise or manifestation of this ability. In a narrow sense, agent usually refers to the manifestation of intentional action; accordingly, the word agent refers to an entity with desires, beliefs, intentions and the ability to act. It should be noted that agents include not only human individuals, but also other entities in the physical and virtual worlds. Importantly, the concept of agent involves the autonomy of individuals, giving them the ability to exercise their will, make choices and take actions, rather than passively responding to external stimuli.
Generative AI is entering the agent era, with “agentic AI” or “AI agent ” being the buzzwords right now . The agent architectures and early use cases we see today represent only the beginning of a broader transformation that promises to redefine the human-machine dynamic, with profound implications for enterprise applications and infrastructure .
Imagine that every decision in an enterprise can be based on in-depth data analysis, customer service can be personalized to each users needs, and internal processes are automated to the point where human supervision is almost unnecessary. This is not a futuristic fantasy, but an enterprise revolution led by AI Agents , which is rapidly changing our business world.