🌟 [ 1 ] The CORE IDEA of AI Agents Explained [ @ ]
( 1733 viewed : 86 like : 17 comments ) The CORE IDEA of AI Agents explained in simple terms.
From the simplest AI agent (LLM with a Python list as memory) to a unified function calling for tool use from Mistral to LLama models.
Why AI Agents need LLM, memory, reasoning and planning capabilities, agents decide based on their information and knowledge provided by different tool use, and then act in their physical environment.
How to learn with Gemini-Pro or GPT-4o new stuff (eg, What is an AI agent?) and explore code examples (written by AI agents) to understand the interplay of single AI agents versus multi-agent configurations and the required task complexity of training datasets.
I showcase how to work and learn with and from AI agents. Human-machine interplay in real time. About the perfect definition of multi-AI-Agents.
When to use a single AI agent vs when to apply multi-agent configurations (with explanation).
00:00 Two AI will help me to understand AI Agents
02:30 First Definition of an AI Agent (mem, plan, ..)
06:45 We build the simplest AI Agent possible
09:30 Planning w/ an LLM and Python list as memory
12:52 Tool use in AI Agents (Code example)
16:17 Function Calling to enhance tool use capabilities
22:20 Decompose complex tasks into Multi-Agent config
25:36 Self-improve, self-reflect & self-learn AI Agents
27:22 Multiple interconnected self improving AI Agents
28:53 Advantages of multi-agent systems
31:25 Advanced Definition of an AI Agent
33:17 RAG in the Agent Framework
34:45 Python Code of simple AI Agent (all functions)
36:27 Complex training data for a complex task
38:21 Process knowledge is a blueprint of how to plan
40:48 Key concept in multi-agent sys and distributed AI
44:55 Code example with multi-agents explained
46:28 GPT-4o evaluates my learning w/ Gemini-Pro
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