Description

What is Agentic AI?

Agentic AI is a type of artificial intelligence that can autonomously plan and take actions to achieve specific goals. Unlike traditional tools such as ChatGPT that mainly respond to prompts, Agentic AI systems can decide what steps to take next. They break down complex objectives into smaller tasks and execute them step by step. These systems can use external tools, APIs, databases, and software to complete real-world actions. Agentic AI includes memory, reasoning, and feedback loops to improve decisions over time.
It can monitor results and adjust its strategy without constant human input.
Businesses use Agentic AI to automate workflows, optimize operations, and build intelligent digital assistants. It represents the next evolution of AI—from generating content to actively achieving outcomes.

Here are key industries that are actively hiring talent in Agentic AI.

Tech & Software: Core R&D in autonomous intelligence, AI platforms, multi-agent systems, and large language models.

Consulting & Professional Services: Advising enterprise clients on automating workflows and deploying agentic solutions.

Finance & FinTech: Automating trading strategies, risk tasks, compliance monitoring, and decision support.

Retail & E-Commerce: Personalized shopping assistants, automated marketing execution, and supply optimization.

Healthcare & Life Sciences: Intelligent agents to assist diagnosis, patient management, clinical workflows.

Education & EdTech: Dynamic learning assistants, automated course generation, adaptive tutoring agents.

Manufacturing & Supply Chain: Agents plan production, monitor systems, optimize logistics, handle exceptions.

Marketing & Advertising: Automated campaign orchestration, audience engagement, performance optimization.

Customer Service & Support: Next-gen support agents that resolve issues and escalate intelligently.

Job Market and How much does an AGENTIC AI consultant make?

Demand remains strong as companies explore autonomous workflows, though some early projects may be reevaluated due to cost and value uncertainty.

Salaries and rates are generally higher than average tech jobs — especially for candidates who combine AI strategy, implementation experience, and domain knowledge. According to recent estimates, salaries in the U.S. range from $135,000 to over $200,000 annually, with senior and specialized roles commanding higher compensation.

The following topics will be covered as part of Agentic AI Master Course.

Module 1: Introduction to Agentic AI and Python GenAI (4 Hours)

Topics :-

What is Agentic AI? Difference from standard AI. – Overview of Python GenAI concepts and frameworks. – Key concepts: autonomous agents, goal-oriented AI, multi-agent systems. – Overview of LangChain, LangGraph, CrewAI, and AutoGen.

Hands-on Activity :-

Set up Python environment with GenAI, LangChain, LangGraph, CrewAI, and run simple agent examples.

Module 2: Python for Agentic AI (6 Hours)

Topics :-

Python basics for AI agents. – Libraries: NumPy, Pandas, requests, LangChain SDK. – Building functions for agent reasoning, automation, and voice output.

Hands-on Activity :-

Implement Python scripts to interact with APIs, automate tasks, and generate voice responses using text-to-speech libraries (e.g., pyttsx3, gTTS).

Module 3: LangChain for Agentic AI (6 Hours)

Topics :-

Chains and Agents in LangChain. – Prompt templates, memory integration, multi-step reasoning. – Tool integration and API calls with optional voice feedback.

Hands-on Activity :-

Build a LangChain agent that fetches data, summarizes it, and provides voice output.

Module 4: LangGraph for Workflow Visualization (6 Hours)

Topics :-

LangGraph overview and node design. – Visual workflow creation and debugging. – Integrating LangGraph workflows with voice-enabled agents.

Hands-on Activity :-

Design a multi-step workflow that performs tasks and provides audio feedback.

Module 5: CrewAI for Multi-Agent Coordination (5 Hours)

Topics :-

CrewAI architecture for multi-agent collaboration. – Communication protocols, shared goals, and voice notifications. – Monitoring performance and error handling.

Hands-on Activity :-

Deploy multiple agents in CrewAI coordinating tasks with audible alerts and status updates.

Module 6: AutoGen for Autonomous Task Execution (6 Hours)

Topics :-

AutoGen setup and agent capabilities. – Autonomous task execution with optional voice feedback. – Integration with LangChain, LangGraph, and APIs.

Hands-on Activity :-

Build an AutoGen agent that automates multi-step processes with voice narration for steps.

Module 7: RAG (Retrieval-Augmented Generation) Agents (5 Hours)

Topics :-

RAG concepts and knowledge-based agents. – Implementing RAG agents using LangChain/AutoGen. – Voice-enabled knowledge response generation.

Hands-on Activity :-

Create an agent that answers questions using external sources and provides voice responses.

Module 8: Planning, Autonomy, and Advanced Automation (6 Hours)

Topics :-

Goal prioritization, task decomposition, and utility functions. – Planning algorithms in LangGraph and AutoGen. – Handling failures, retries, and voice alerts in workflows.

Hands-on Activity :-

Build an autonomous workflow with conditional branching, self-correction, and audio notifications.

Module 9: Safety, Ethics, and Explainability (5 Hours)

Topics :-

Agentic AI safety, alignment, and limits. – Ethical considerations and bias mitigation. – Logging, explainability, and optional voice reports.

Hands-on Activity :-

Evaluate agent decisions, create explainability reports, and generate voice summaries.

Module 10: Capstone Project – Complete Voice-Enabled Agentic AI System (6 Hours)

Topics :-

Build a fully autonomous, voice-enabled agentic AI system integrating: – Python GenAI for intelligent processing. – LangChain for task chaining and reasoning. – LangGraph for workflow visualization. – CrewAI for multi-agent coordination. – AutoGen for autonomous task execution with voice output. – Deliverable: Working system demo with voice-enabled interactions + documentation explaining workflow, decision-making, and RAG integration.

Course Features :-

Mode:

Theory + Hands-on + Case Studies – Tools: Python, LangChain, LangGraph, CrewAI, AutoGen, Text-to-Speech libraries, APIs, RAG sources. – Outcome: Participants can design, deploy, automate, and monitor complex voice-enabled agentic AI systems for real-world applications.