Why Entire AI field is headed towards AI Agents

GenAI is just the beginning, what comes next is AI Agent.

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📝 Why Read It?
Understand how AI agents will reshape industries and careers, enabling you to stay ahead in the rapidly evolving technological landscape.
Learn about creating AI agents and harnessing their power as an early adopter.

🤔 Why do we need AI Agents when we have LLMs and RAG?
AI agents offer goal-oriented behavior, memory/state tracking, interaction with the environment, better transfer and generalization, continual learning, and multi-task capabilities.
They bridge the gap between language understanding and real-world action.

🌍 How the World Will Change With "AI Agents"?
AI agents can handle complex, multi-step tasks like booking trips, including searching, booking, calendar updates, and reminders.
They can process language, make decisions, and take actions, going beyond just knowledge and text generation.

📐 Understanding LLMs, RAG, and AI Agents
LLMs excel at language understanding and generation but lack goal orientation and real-world action.
RAG improves LLMs by finding relevant information but still focuses on knowledge and text.
AI agents bridge the gap, chaining together steps like retrieving information, reasoning, and taking actions.

🧱 What does the Architecture of an AI Agent Entail?
A reasoning engine (LLM) for language understanding and problem-solving.
A knowledge base for storing information, experiences, and preferences.
Tool integration for interacting with software applications and services.
Sensory input for perceiving the environment (text, images, sensors).
(Potentially) A user interface for human-agent collaboration.