Gen AI Part 9 - Introduction to Agentic AI (Create Autonomous Agents)

Описание: Agentic AI refers to autonomous systems that use Large Language Models (LLMs) to perceive their environment, reason through complex goals, and execute actions without constant human intervention. The evolution of this field has led to specialized frameworks that cater to different architectural needs:

LangChain Agents (The Modular Foundation):

Architecture: Built on a "chain-based" philosophy, LangChain provides a massive toolkit for creating modular and deeply customizable single-agent systems.

Core Philosophy: It uses the ReAct (Reason + Act) paradigm, where an agent iteratively thinks and selects tools to solve a step-by-step mission.

Best For: Developers who need granular control over tool integration, memory, and precise, rule-following workflows like document retrieval (RAG).

AutoGen (The Conversational Brain):

Architecture: Developed by Microsoft Research, it treats AI workflows as asynchronous, multi-agent conversations where agents act like specialized chat participants.

Core Philosophy: It emphasizes "LLMs talking to LLMs," allowing agents to negotiate, self-correct code, and even involve humans in the dialogue loop.

Best For: Complex, iterative tasks requiring dynamic problem-solving, such as automated software engineering, multi-step reasoning, and brainstorming.

CrewAI (The Collaborative Workforce):

Architecture: A higher-level framework that organizes agents into a "Crew" with defined roles, backstories, and specific goals.

Core Philosophy: It prioritizes "roles over rules," using natural language to orchestrate teamwork—for example, a "Researcher" agent handing data to a "Writer" agent.

Best For: Projects that naturally map to human team structures, such as automated market research, content creation, and sequential project execution.