How to learn Agentic AI

Agentic Al

Agentic AI represents the next evolution of artificial intelligence—systems that can reason, plan, take actions, learn from feedback, and operate with a degree of autonomy.

What Is Agentic AI?

Agentic AI refers to AI systems designed to act as agents. These agents can:

  • Understand goals

  • Make decisions

  • Use tools and APIs

  • Interact with users or other agents

  • Learn and improve over time

Unlike traditional AI models that respond once per prompt, agentic systems operate in continuous loops of perception, reasoning, and action.

Agentic AI vs Traditional AI Systems

Traditional AI:

  • Responds to single prompts

  • Has limited memory

  • Cannot independently take actions

  • Requires constant human input

Agentic AI:

  • Works across multiple steps

  • Maintains short-term and long-term memory

  • Uses tools and external systems

  • Can collaborate with humans or other agents

Types of AI Agents

Reactive Agents

Respond immediately to inputs without memory.

Proactive Agents

Take initiative and anticipate future needs.

Goal-Driven Agents

Plan and execute tasks to achieve defined objectives.

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Agent Workflows

Agentic systems typically follow this workflow:

Perception

Understanding input, environment, or data

Reasoning

Analyzing options and planning steps

Action

Executing tasks using tools or APIs

Learning

Improving behavior through feedback

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Agent Communication

Agents can communicate in multiple ways:

Agent-to-Agent communication

Human-to-Agent collaboration

Multi-agent coordination for complex tasks

Querying & Interaction

  • Prompt engineering for agents

  • Multi-turn conversations

  • Context and state management

  • Natural language interfaces

  • Action execution via APIs, databases, and applications

To build effective agents, you must understand interaction techniques such as:

Core Components & Architectures

Agentic AI systems are built using several core components:

Agent Frameworks

Popular frameworks include:

  • LangChain

  • AutoGen

  • CrewAI

These frameworks help structure agent behavior, memory, and tool usage.

Reasoning & Planning

Advanced agents use:

  • Chain of Thought reasoning

  • Tree of Thought reasoning

  • LLM-based planning systems

These techniques help agents break down complex problems and make better decisions.

Memory

Agents rely on memory to improve performance:

  • Short-term memory for conversations

  • Long-term memory for knowledge retention

  • Contextual memory for reasoning continuity

Tool Use

Agents interact with external systems using:

  • API integrations

  • Function calling

  • Database queries

  • Third-party services

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Tools & Ecosystems

Modern agentic systems use a rich ecosystem of tools:

Agent Frameworks

  • LangChain

  • AutoGen

  • CrewAI

  • Haystack

Vector Databases

  • Pinecone

  • Weaviate

  • FAISS

  • Milvus

Orchestration Tool

  • n8n

  • Make

  • Apache Airflo

Large Language Models (LLMs)

  • GPT-4 / GPT-4.5

  • Claude

  • LLaMA

  • DeepSeek

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