What is Agentic AI?

Agentic AI refers to a new generation of Artificial Intelligence systems designed to act autonomously, reason, plan, and execute tasks toward achieving defined goals — with minimal human intervention.

Unlike traditional AI models that simply respond to queries, Agentic AI systems behave like intelligent agents that can make decisions, use tools, interact with software or other agents, learn from feedback, and complete multi-step objectives independently.

Simple Definition

Agentic AI is AI that can think, plan, and act on its own to accomplish goals.

How Agentic AI is Different from Traditional AI

Feature	Traditional AI / Chatbots	Agentic AI
Interaction	Responds to user queries	Acts autonomously
Task Handling	Single-step responses	Multi-step task execution
Decision Making	Limited	Dynamic reasoning & planning
Tool Usage	Rare	Uses APIs, databases, software tools
Memory	Short context only	Persistent long-term memory
Learning	Static responses	Improves via feedback loops
Collaboration	Single model	Multi-agent teamwork

Core Capabilities of Agentic AI

🔹 Autonomy

Agents can operate without continuous human input.

🔹 Reasoning

They break down complex problems into smaller steps.

🔹 Planning

They decide sequences of actions to achieve goals.

🔹 Tool Use

They call APIs, search the web, access databases, or run software.

🔹 Memory

They retain past interactions and knowledge.

🔹 Self-Correction

They evaluate results and improve actions.

🔹 Collaboration

Multiple agents can work together as teams.

Example

Traditional Chatbot:
User: “Find me a marketing report on telecom trends.”
Bot: Gives links or text.

Agentic AI:

Searches web and databases
Extracts relevant data
Summarizes findings
Creates a formatted report
Emails it to the user
Asks for feedback
(All done autonomously)

Common Use Cases

Autonomous customer support agents
AI research assistants
AI coding and debugging agents
Business analytics agents
AI recruitment and HR agents
Autonomous trading agents
Smart IoT and robotics control agents

Technologies Behind Agentic AI

Large Language Models (LLMs)
Reasoning & Planning frameworks
Agent frameworks (LangChain, AutoGen, CrewAI)
Vector databases for memory
Tool APIs and web integration
AgentOps for monitoring

Agentic AI creates intelligent digital workers that can independently plan and perform complex tasks.

4-Month Training Course on Agentic AI

Course Title

Agentic AI Engineer Program – Building Autonomous Multi-Agent Systems

Course Vision

This program prepares participants to become Agentic AI Engineers capable of designing, building, and deploying autonomous AI agents and multi-agent ecosystems using the latest LLM frameworks, orchestration tools, vector databases, and AgentOps platforms.

The course balances conceptual foundations, framework-based implementation, and enterprise deployment practices.

Program Outcomes

By the end of this course, participants will:

Architect intelligent autonomous AI agents
Implement reasoning, planning, memory, and tool usage
Build single-agent and multi-agent systems
Integrate agents with enterprise applications
Deploy scalable agentic systems on cloud
Apply governance, observability, and Responsible AI
Deliver a real-world capstone Agentic AI solution

Target Audience

AI Developers • ML Engineers • Software Architects • Data Scientists
IT Professionals • Product Managers • Digital Transformation Leaders

Duration

4 Months (16 Weeks)
3 Instructor-led sessions/week + Weekly Labs + Capstone Project

PROGRAM STRUCTURE

MONTH 1

Foundations of Agentic AI & LLM Engineering

Week 1 — Introduction to Agentic AI

Evolution: Automation → Chatbots → Agents → Agentic AI
Characteristics of autonomous agents
Agent vs Workflow vs Copilot vs Multi-Agent
Industry applications and opportunities

Week 2 — Large Language Model Foundations

LLM architectures and capabilities
Prompt Engineering & Structured Prompts
Function Calling & Tool Invocation
LLM APIs (OpenAI, Anthropic, Open Models)

Week 3 — Agent Architectures & Design Patterns

Reactive Agents
Goal-based Agents
Planner-Executor Agents
ReAct Pattern
Tree-of-Thought & Chain-of-Thought

Week 4 — Reasoning, Planning & Task Decomposition

Task breakdown strategies
Autonomous goal execution loops
Self-reflection & correction
Agent evaluation methods

Frameworks Introduced:
OpenAI Function Calling • PromptFlow • Guidance • DSPy

Outcome:
Design reasoning-based single agents.

MONTH 2

Building Intelligent Agents with Frameworks

Week 5 — Core Agent Frameworks

LangChain – Agent chains & tool calling
LlamaIndex – Knowledge & retrieval agents
Semantic Kernel (Microsoft) – Enterprise agent orchestration
Haystack Agents – Open-source agent pipelines

Week 6 — Tool-Using Agents & API Integration

Connecting agents with external tools
REST / GraphQL API calling by agents
Database and SaaS integration
Web browsing and data extraction agents

Week 7 — Memory & Knowledge Systems

Short-term & long-term agent memory
Vector Databases: Pinecone, Weaviate, Chroma, FAISS
Retrieval Augmented Generation (RAG)
Persistent knowledge agents

Week 8 — Autonomous Execution & Feedback Loops

Agent loops and state machines
Error handling and retries
Self-improving agents
Performance benchmarking

Frameworks Mastered:
LangChain • LlamaIndex • Semantic Kernel • Vector DB Stack

Outcome:
Build full tool-using, memory-enabled intelligent agents.

MONTH 3

Multi-Agent Systems & Enterprise AgentOps

Week 9 — Multi-Agent Collaboration

Multi-agent communication protocols
Role-based agent teams
Delegation & negotiation
Coordination strategies

Week 10 — Multi-Agent Frameworks

AutoGen (Microsoft) – Conversational agent orchestration
CrewAI – Role-based multi-agent workflows
MetaGPT – AI software team simulation
OpenAI Swarm / Agents SDK concepts

Week 11 — Enterprise Integration

Agents with CRM, ERP, HRMS
Workflow automation with agents
Security & access management
Private LLM deployment strategies

Week 12 — AgentOps, Monitoring & Governance

Observability and tracing
Agent performance analytics
LangSmith / PromptFlow Monitoring
Responsible AI & compliance
Explainability for autonomous decisions

Frameworks Mastered:
AutoGen • CrewAI • MetaGPT • LangSmith • PromptFlow

Outcome:
Build enterprise-ready multi-agent ecosystems.

MONTH 4

Deployment, Scaling & Capstone

Week 13 — Deployment & Scaling

Containerizing agents with Docker
Cloud deployment (AWS, Azure, GCP)
Serverless agent hosting
Cost optimization strategies

Week 14 — Human–AI Collaboration

Human-in-the-loop design
Trust calibration
AI safety for autonomous agents

Week 15 — Capstone Project Development

Participants build an end-to-end system such as:

Autonomous research agent
AI customer service agent
AI business analyst agent
AI software development team

Week 16 — Presentation & Future Trends

Project demonstration
Agentic AI roadmap
Career pathways: Agent Engineer, AI Architect

Final Outcome:
Portfolio-ready production-grade Agentic AI system.

COMPLETE FRAMEWORK STACK COVERED

Agent Frameworks

LangChain • LlamaIndex • Semantic Kernel • Haystack

Multi-Agent Frameworks

AutoGen • CrewAI • MetaGPT • Swarm Concepts

Reasoning & Prompt Frameworks

DSPy • Guidance • PromptFlow • ReAct

Memory & Knowledge Stack

Pinecone • Weaviate • Chroma • FAISS • RAG Pipelines

AgentOps & Monitoring

LangSmith • PromptFlow Monitoring • OpenTelemetry

Deployment Stack

Docker • FastAPI • Cloud Run • AWS Bedrock • Azure AI Studio

CERTIFICATION

Certified Agentic AI Engineer

Autonomous Systems Development

Join our 4-month Agentic AI training course and become a certified Agentic AI Engineer. Learn to build autonomous AI agents and multi-agent systems using LangChain, AutoGen, CrewAI, LlamaIndex, Semantic Kernel, vector databases, AgentOps, and cloud deployment. Hands-on projects and enterprise-focused curriculum included.

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Agentic AI Training Course