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Agentic AI Course

Learn how to design, coordinate, and deploy production-grade AI agents in this live online Agentic AI Certification Course built for engineers and serious working professionals.

The course moves from first principles to real architectures using LangChain, LangGraph, CrewAI, AutoGen, MCP, Agentic RAG, evaluation, and tracing through guided labs, structured projects, certification, and a capstone.

12 WeeksLive Instructor-Led8+ Projects + CapstoneCourse Certificate
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What Is the Agentic AI Course at School of Core AI?

A 12-week live, mentor-led online program that teaches you to build agentic AI — autonomous systems that reason, plan, and use tools — and deploy them to production using modern agent frameworks. The fee is ₹35,000 for the full program with 8+ hands-on projects, mentor code reviews, a portfolio, and a completion certificate.

What Will You Build in This Agentic AI Course?

  • Multi-agent customer-support pipeline
  • RAG-powered research & retrieval agent
  • Autonomous code-review & DevOps agent
  • Enterprise workflow automation system

Who Is This Agentic AI Course For?

  • Software & ML engineers building agent systems
  • Tech leads & architects designing AI workflows
  • Platform & backend engineers integrating agents
  • Engineering leaders driving AI-native products

Program Overview

A comprehensive training program designed to transform developers into autonomous AI system architects, covering everything from agent fundamentals to production deployment.

12 Weeks
Duration
8+ Hands-on
Projects
Live + Recorded
Mode
On Completion
Certificate
01

Autonomous Agent Development

Build self-governing AI agents capable of reasoning, planning, and executing complex tasks without constant human oversight.

02

Multi-Agent Orchestration

Design collaborative agent networks using LangGraph, CrewAI, and AutoGen for enterprise-scale workflow automation.

03

Advanced Reasoning Patterns

Master ReAct, Chain-of-Thought (CoT), and Tree-of-Thought (ToT) prompting strategies for intelligent decision-making.

04

Production-Ready Deployment

Deploy secure, scalable AI agents with MCP integration, observability tools, and enterprise-grade guardrails.

What You'll Learn

Master autonomous AI agent development with LangGraph, CrewAI, and AutoGen. Learn ReAct, CoT, and ToT reasoning patterns while building production-ready multi-agent systems.

Career Outcomes

Become an AI Agent Engineer, Automation Architect, or AI Solutions Developer. Work with cutting-edge agentic AI frameworks used by leading tech companies worldwide.

Why Engineers Choose This Program

9 reasons engineers and product leaders pick this program.

Build Real Autonomous Agents

Design and deploy agents with reasoning, memory, and multi-step workflows across real-world tasks.

Multi-Agent System Expertise

Coordinate teams of agents using CrewAI and LangGraph to solve complex enterprise problems.

Hands-on with LangChain & AutoGen

Master the most advanced frameworks to build, debug, and optimize agent-based applications.

Enterprise-Ready RAG & Memory

Fuse retrieval with long-term memory for smarter agent cognition using vector DBs and LangChain.

PromptOps & Agent Reasoning

Implement advanced prompting patterns—ReAct, CoT, ToT—for higher-quality agent decisions.

Secure Agent Deployment (MCP)

Deploy agents using Model Context Protocol (MCP), sandboxing, and secure function calling.

AgentOps, Tracing & Guardrails

Learn how to monitor, trace, and apply cost-control guardrails to production agent workflows.

SDKs for Real Deployment

Access OpenAI SDK, Google A2A, CrewAI, and LangGraph in real projects and GitHub-ready stacks.

Mentorship from Agentic Engineers

Learn from engineers building agent-based systems in real startups, products, and research labs.

What Sets This Course Apart

What you get in this program — and why each piece matters for real agent work.

Multi-Agent Architectures

Included: Build orchestrated systems with LangGraph, AutoGen, and CrewAI — context sharing, handoffs, and state.
Why it matters: Real products need coordinated agents, not single prompt calls.

Agent Reasoning (PromptOps)

Included: Apply CoT, ReAct, ToT, and ReWOO patterns for step-by-step planning and tool use.
Why it matters: Structured reasoning is what makes an agent reliable instead of random.

Secure Deployment (MCP)

Included: Use Model Context Protocol for scoped tool calling, sandboxing, and authenticated execution.
Why it matters: Production agents touch real systems, so access has to be controlled and auditable.

RAG with Memory & Retrieval

Included: Ground agents with long-term memory, vector search, and contextual retrieval.
Why it matters: Agents that remember and cite context give accurate, trustworthy answers.

Live Mentorship & Reviews

Included: Architecture reviews, use-case mentoring, and code-level debugging from practitioners.
Why it matters: Feedback on your own build is where the real learning happens.

Capstone & Certificate

Included: Ship versioned agent projects to a GitHub portfolio plus a course completion certificate.
Why it matters: You leave with proof of work you can walk an interviewer through.

Placement Support

Included: Resume review, project narration, mock interviews, and referral connections.
Why it matters: Skills plus a clear job path, not just a recording library.

Skills You'll Gain

Real engineering skills interviewers test for AI Agent & Automation roles — not just tool names.

Autonomous Agent DesignMulti-Agent OrchestrationReAct & Chain-of-Thought ReasoningTree-of-Thought PlanningAgent Memory ArchitectureContext-Window ManagementRAG Pipeline EngineeringPrompt Engineering for AgentsFunction-Calling & Tool UseSecure Agent Deployment (MCP)AgentOps & ObservabilityCost & Token OptimizationWorkflow Automation DesignError Recovery & GuardrailsAgent Testing & EvaluationMulti-Modal Agent Coordination

Industry-Ready: Every skill here is mapped to real-world job requirements for AI agent and automation roles.

Tools & Frameworks You'll Use

Gain hands-on expertise with industry-leading tools powering autonomous AI systems at top technology companies worldwide.

Orchestration

LangGraph

Graph-based framework for building reactive, persistent multi-agent workflows with state management.

Workflow DesignState MachinesAgent Coordination
Collaboration

CrewAI

Role-based agent collaboration framework enabling task delegation and parallel agent execution.

Team AgentsTask DelegationRole Assignment
Multi-Agent

AutoGen

Microsoft's conversational multi-agent framework supporting multi-turn dialogue and tool usage.

ConversationsCode GenerationProblem Solving
Foundation

OpenAI SDK

Core SDK for GPT models with function calling, structured outputs, and assistant capabilities.

Function CallingAssistants APIEmbeddings
Framework

LangChain

Comprehensive framework for building LLM applications with chains, agents, and memory systems.

ChainsAgentsMemory
Observability

LangSmith

Debug, trace, and evaluate prompt chains and agent runs with visual insights and analytics.

TracingEvaluationDebugging
Security

Model Context Protocol

Secure, auditable LLM-to-tool communication via WebSocket or SSE with sandboxed execution.

Tool AccessSecurityAudit Logs
Vector DB

Qdrant & FAISS

High-performance vector databases for semantic search, embeddings storage, and RAG pipelines.

EmbeddingsSimilarity SearchRAG
8+
Enterprise Frameworks
8+
Hands-on Projects
100%
Production Ready

Who Is This Course For?

Choose the path that matches your role. All tracks include live sessions, projects, and certification.

Product & Workflow Track

Product / Project Managers

UI-based, workflow & no-code focus

  • Automate business workflows with Zapier, Make, and n8n
  • Evaluate and deploy no/low-code AI agents for teams
  • Understand RAG, prompt engineering, and agent capabilities
View curriculum
Engineering & Architecture Track

Engineers (Software / ML / AI / DS)

Deep architecture, production-grade agents

  • Build multi-agent systems with LangGraph, AutoGen, and CrewAI
  • Master ReAct, CoT, ToT reasoning + MCP secure deployment
  • Ship agent pipelines with AgentOps, tracing, and guardrails
View curriculum
Enterprise Cohort

Enterprise Teams

Upskilling, ROI, and governance

  • Custom cohorts for 5–50+ employees with tailored pace
  • AI governance, cost control, and compliance guardrails
  • Measurable ROI: agent-driven process gains in 12 weeks
View curriculum

Prerequisites (kept practical): Basic Python or prompt-engineering knowledge is helpful. We cover all essential concepts before advancing to multi-agent architectures.

Want the full delivery role? Agentic AI is one of the three pillars \u2014 with the AI Developer Course and LLMOps Course \u2014 of our Forward Deployed Engineer Course, a production-focused role program.

Course Roadmap: 12 Modules, 12 Weeks

12 modules over 12 weeks — from agent fundamentals to production-grade capstone projects.

Module 1

01.Agentic AI Foundations

  • Agent types: Reactive, Goal, Utility, Learning
  • Core loop: Sense → Think → Act → Learn
  • Build your first agent using LangChain
Module 2

02.Build Agents with AutoGen

  • Roles, chats, tool routing in AutoGen
  • Tool chaining and data pipelines
  • Project: Email/report writing agent
Module 3

03.LangGraph Agent Workflows

  • Nodes, edges, retry/fallback patterns
  • Memory + condition-based routing
  • Project: Customer support bot
Module 4

04.PromptOps & Reasoning

  • CoT, ReAct, Tree of Thought, DSP
  • Prompt tuning for structured tools
  • Function-calling APIs and refinement
Module 5

05.Multimodal Architectures

  • CrewAI for text teams
  • Vision agents: CLIP, LLaVA
  • Voice agents: Whisper, XTTS
Module 6

06.Multi-Agent Collaboration

  • Role assignment and task splits
  • Chat-based vs orchestration agents
  • Lab: Transcribe → Fetch → Visualize
Module 7

07.Retrieval-Augmented Agents

  • Chunking, embedding, hybrid retrieval
  • Smart prompt merging with memory
  • Project: PDF chat + assistant agent
Module 8

08.Model Context Protocol

  • MCP client-server setup
  • Protocols: SSE, WebSocket
  • Use case: Agent-to-API bridge
Module 9

09.Agent SDKs & Frameworks

  • OpenAI Agents SDK: Tooling + Handoffs
  • Google A2A for decentralized agents
  • Compare: LangGraph vs AutoGen
Module 10

10.AgentOps & Monitoring

  • LangSmith, SuperAgent, Helicone
  • Cost tracing, error recovery
  • Guardrails, fallback strategies
Module 11

11.Agent Design Patterns

  • Centralized vs distributed flows
  • Stateless vs stateful memory patterns
  • CoT, ReAct, Event-driven agents
Module 12

12.Capstone Agent Projects

  • RAG-powered tutor agent
  • Debug assistant with memory
  • Multi-agent sales bot with vision
View the full week-by-week Agentic AI roadmap →

Detailed Curriculum & Syllabus

Choose your path: Business workflow automation or production-grade AI agent engineering. Enterprise cohorts available.

Download Brochure

Your Agentic AI Course Certificate

Complete the program and receive a course completion certificate from the School of Core AI that reflects the agent systems you built — from autonomous agents and multi-agent orchestration to LangGraph workflows and production deployment.

Sample certificate

CERTIFICATE

OF ACHIEVEMENT

THIS IS TO CERTIFY THAT

SCHOOL
OF
CORE
AI

YOUR NAME

Date : DD MMM YY

Has Successfully Completed The

Comprehensive Agentic AI Engineering Program

Conducted By The School Of Core AI.

This Intensive Program Included Hands-On Training In LangGraph, CrewAI, AutoGen, Multi-Agent Systems, PromptOps (ReAct, CoT, ToT), RAG Pipelines, Model Context Protocol (MCP), Agent Memory Systems, LangSmith Observability, And Production-Grade Agentic AI Deployment.

Aishwarya Pandey

Founder and CEO

Share your certificate on LinkedIn, add it to your portfolio, or bring it to interviews as evidence of the agent systems you built during the program.

Agentic AI Course Fees & Enrollment

One all-inclusive fee for 12 weeks of Live ILT, guided projects, capstone demo, and a course completion certificate.

Admissions openNext live batch: 15th–30thSmall batches

One-time payment

₹35,000

12 weeks • Live ILT • Capstone

Duration: 12 weeks
Format: Live + Recorded
Projects: 8+ hands-on + capstone
Certificate: on completion
Enroll / Get Fee DetailsTalk to our team: +91 96914 40998

We confirm exact batch timings and schedule fit during the call.

₹35,000 includes Live ILT, guided projects, capstone, certificate, and placement support — no hidden charges.

What you'll get

  • Live instructor-led sessions with clear milestones on LangGraph, CrewAI, AutoGen.
  • 8+ portfolio-grade agent builds + 1 capstone multi-agent project.
  • Code reviews, debugging help, and agent architecture guidance.
  • Interview + portfolio support (resume review, project narration, mock rounds).
  • Lifetime access to recordings and updated agent patterns you can reuse at work.

Best for working developers: plan for ~8–10 hrs/week (live sessions + build time).

Get the complete enrollment planCall: +91 96914 40998

Agentic AI Course fees are 35,000 INR for a 12 week live instructor-led training program with hands-on projects, capstone demo, and a course completion certificate.

What Our Learners Say

Real journeys from engineers and analysts who built agent systems in this program.

"I came in able to call an LLM API and left able to design a multi-agent system. Building the LangGraph support pipeline end-to-end — with memory, retries and tracing — is what finally made agents click for me."
Rahul S.
Software Engineer moving into AI agents
"The part I valued most was orchestration. We didn't just chain prompts — we built agents that route tools, recover from errors and stay observable. The MCP and function-calling sections were directly useful at work."
Yusuf J.
Backend Engineer
"Coming from analytics, agentic design felt intimidating. Working through RAG, vector search and tool use one project at a time made it approachable, and the code reviews caught mistakes I couldn't see myself."
Nitin G.
Data Analyst transitioning to AI
"The reasoning-pattern module — ReAct, CoT and ToT — changed how I structure agents. Pairing it with LangSmith tracing meant I could actually debug why an agent made a decision instead of guessing."
Om Y.
Machine Learning Engineer
"It's a genuinely hands-on program. By the capstone I had a multi-agent project I could talk through in interviews — architecture choices, trade-offs and all — not just a certificate."
Sneha M.
AI Engineer
"What stuck with me was production thinking — guardrails, cost control and evaluation treated as defaults, not extras. That's the difference between a demo and something you can actually ship."
Rohit R.
Full-Stack Developer

Compare Before You Enroll

Check whether you need agentic depth or a neighboring path

These comparisons help you decide whether your next step is agent systems, broader GenAI foundations, or application-building first.

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AI Developer Course vs Agentic AI Course

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Generative AI Course vs Agentic AI Course

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Learning Track

RAG vs Agentic RAG

Choose between standard retrieval pipelines and more agentic, tool-using retrieval workflows.

Open comparison
Tool

CrewAI vs AutoGen vs LangGraph

Compare three agent frameworks by speed, collaboration style, and orchestration control.

Open comparison

Agentic AI Course — Frequently Asked Questions

Everything you need to know about the Agentic AI Course.

Got More Questions?

Talk to Our Team Directly

Contact us and our academic counsellor will get in touch with you shortly.

Enquire Now +91 96914 40998 info@schoolofcoreai.com
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