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What is Agentic AI?

Discover what makes AI agents different from traditional LLMs and why they represent the next evolution in AI

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Introduction

Core Concepts

Interactive Demo

Practical Application

Key Takeaways

Core Concepts

Let's dive deeper into the architectural components that make AI agents truly autonomous and capable.

The Agent Architecture

An agentic AI system consists of several interconnected components working in harmony:

🧠

Reasoning Engine

The "brain" powered by an LLM (GPT-4, Claude, Llama). Handles understanding, planning, and decision-making.

🔧

Tool Interface

Functions the agent can call: APIs, databases, calculators, web searches, file systems, external services.

💾

Memory System

Short-term (conversation context) and long-term (vector database) storage for facts, experiences, and learned patterns.

🎯

Planning Module

Breaks complex goals into subtasks, sequences actions, handles dependencies, and adapts when plans fail.

⚖️Interactive Comparison: LLM vs Agent

How it Works

Input → Process → Output. Single-turn interaction with no ability to take actions or persist context.

✗ Cannot use external tools or APIs

✗ No memory between conversations

✗ Cannot break down complex tasks

✗ Limited to knowledge cutoff date

✓ Fast response times

✓ Predictable behavior

The ReAct Pattern: Reasoning + Acting

The breakthrough that enabled practical agentic AI is the ReAct (Reasoning + Acting) pattern, introduced by researchers at Princeton and Google in 2023.

ReAct Loop Example:

Thought:

I need to find the current weather in Tokyo

Action:

search_weather({"location": "Tokyo, Japan"})

Observation:

Temperature: 18°C, Condition: Partly cloudy

Thought:

Now I can provide the user with accurate current weather

Answer:

The current weather in Tokyo is 18°C and partly cloudy.

This iterative loop of thinking → acting → observing enables agents to handle complex, multi-step workflows that would be impossible for a traditional LLM.

Agent Types & Patterns

🔄 ReAct Agent

Iterative reasoning and action loop. Best for research, data analysis, complex problem-solving.

📋 Plan-Execute Agent

Creates full plan upfront, then executes. Efficient for well-defined tasks.

🪞 Reflexion Agent

Self-reflects on mistakes and improves. Learns from failed attempts.

👥 Multi-Agent System

Multiple specialized agents collaborate. Each has unique expertise.

💡 Pro Tip: Modern agent frameworks like LangChain and AutoGen abstract these patterns into reusable components, letting you build sophisticated agents without implementing the low-level logic yourself.