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Agentic AI Development Course

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Agentic AI Development Course

Become a Master in Building Autonomous AI Agents for Real-World Applications

Course Objective

To equip learners with in-demand skills for building intelligent, autonomous, and collaborative AI agents using modern frameworks and platforms. The course is designed for developers, data scientists, AI enthusiasts, and business professionals aiming to implement practical AI solutions.

Course Format

  • Duration: 12 Weeks (3 months)
  • Mode: Online (Instructor-led or Self-paced)
  • Project-based Learning: 7+ Real-life Projects
  • Certification: Issued upon successful completion and final project submission.
  • Tools & Platforms: LangChain, LangGraph, Cohere, LlamaIndex, Phidata, CrewAI, Autogen, Azure, AWS Bedrock, and more.

Curriculum Outline

Module 1: Foundations of Agentic AI

  • What is Agentic AI?
  • Agentic AI vs Traditional AI vs Generative AI
  • Key building blocks: Autonomy, Reasoning, Memory, Human-in-the-Loop
  • AI Architectures: Single vs Multi-Agent Systems
  • Ethical AI and Governance

Module 2: Agentic AI Architectures & Design Patterns

  • Cognitive and Perception Modules
  • ReAct and ReWOO Patterns
  • Tool Use, Planning, and Reflection Patterns
  • Secure and Scalable Agentic Designs

Module 3: Building AI Workflows with LangChain & LCEL

  • Runnables and Chains
  • Integration with Vector Databases
  • LangChain Expression Language (LCEL)
  • Build: Self-Correcting AI Coding Assistant

Module 4: Designing Stateful Agents with LangGraph

  • State Schema, Reducers, Memory Design
  • UX and Human Feedback Loops
  • Deploy: LangGraph-powered Finance Bot

Module 5: Retrieval-Augmented Generation (Agentic RAG)

  • Adaptive RAG Architectures
  • LlamaIndex and Cohere Integrations
  • Build: Market Research Agent

Module 6: Rapid AI Agent Development with Phidata

  • Workflow Management
  • Embeddings, Vector Stores
  • Build: Data Analysis Agent

Module 7: Multi-Agent Collaboration with LangGraph & CrewAI

  • Multi-Agent Workflows
  • Role Assignments & Orchestration
  • Build: Customer Support Bot + Stock Market Analyst

Module 8: Autogen for Advanced Agent Communication

  • Role-based Conversations
  • Tool Chaining and Human Oversight
  • Build: Autonomous AI Research Agent

Module 9: Observability and AI AgentOps

  • Using Langfuse and Langsmith
  • Tracing, Logging, Performance Metrics
  • Build: Real-time Monitoring Dashboard for Agents

Module 10: Low-Code/No-Code AI Agent Development

  • Tools: Langflow, Relevance AI, Wordware
  • Designing Workflows without Code
  • Build: SEO Agent + Content Writer Assistant

Bonus Module: Cloud Deployment of AI Agents

  • AWS Bedrock, Azure OpenAI, Google Vertex AI
  • No-Code Agent Deployment on Cloud
  • Governance, Scaling, and Maintenance
  • Build: Multi-cloud Deployed AI Agents

Final Capstone Project

Build a Full-fledged AI Agent System

Choose a real-world problem (e.g., Healthcare, Education, Finance, E-commerce) and develop an end-to-end AI agent solution with observability and cloud deployment.

Who Should Enroll?

  • AI Developers & Engineers
  • Data Scientists & ML Engineers
  • Tech Entrepreneurs & Product Managers
  • Students aiming for AI Careers
  • Anyone curious about building intelligent agents

Tools & Frameworks Covered

LangChain, LCEL, LangGraph, Autogen, Phidata, CrewAI, Langfuse, Langsmith, LlamaIndex, Cohere, Wordware, Relevance AI, Azure ML, AWS Bedrock, GCP Vertex AI.