Master In Agentic AI

Python for AI & Automation (Foundation Layer) builds core Python skills to automate tasks and create a strong base for AI and machine learning development.

A foundational overview of how autonomous AI agents work with large language models to plan, reason, and execute tasks across modern AI systems.

An introductory overview of using LangChain and LangGraph to build, connect, and orchestrate intelligent, multi-step AI workflows.

A concise introduction to building stateful, multi-step AI workflows using LangGraph for agent orchestration and decision-making.

 An overview of setting up the Python SDK and configuring nodes to build, connect, and manage scalable AI workflows efficiently.

A practical introduction to designing and orchestrating multiple AI agents that collaborate and share state using LangGraph.

An overview of managing, passing, and maintaining context across nodes to enable consistent and intelligent decision-making in graph-based AI workflows.

A beginner-friendly overview of AutoGen for building and coordinating conversational AI agents that collaborate to solve complex tasks.

A practical guide to designing, configuring, and deploying tailored AI agents using the AutoGen framework.

 An introduction to integrating AutoGen agents with LangGraph to build scalable, coordinated, and state-aware multi-agent AI systems.

A practical overview of using LangGraph to extract, validate, and process invoice data through structured, multi-step AI workflows.

An overview of building an OCR-based pipeline to extract, clean, and structure text from images for automated data processing.

A focused overview of designing more complex graph-based AI workflows with conditional logic, state management, and optimized execution.

A beginner-friendly overview of using Docker to package, deploy, and run applications consistently across different environments.

An introduction to orchestrating multiple services using Docker Compose for streamlined development and deployment workflows.

An overview of validating and testing LangGraph applications running in Docker to ensure reliability, scalability, and smooth deployments.

 A beginner-level overview of Kubernetes for orchestrating, scaling, and managing containerized applications in production environments.

An overview of deploying and managing LangGraph-based AI workflows on Kubernetes for scalable and resilient production setups.

A concise overview of deploying and scaling AutoGen-based multi-agent systems on Kubernetes for production-ready AI workloads.

An introduction to deploying, managing, and scaling applications on Microsoft Azure using secure, reliable cloud services.

An introduction to deploying, managing, and scaling applications on Amazon Web Services using secure, scalable, and cost-effective cloud infrastructure.

An overview of implementing automated build, test, and deployment pipelines to continuously deliver reliable and scalable agentic AI systems.

An overview of designing, deploying, and maintaining robust agentic AI systems with scalability, security, and real-world reliability.

An overview of monitoring, tracing, and logging AI systems to gain visibility into performance, behavior, and system health.

 An overview of measuring, analyzing, and optimizing system performance to ensure efficient, reliable, and scalable AI applications.

An in-depth look at designing optimized prompts that improve reasoning, coordination, and decision-making in AI agents.

An overview of incorporating user feedback to continuously refine, adapt, and improve agent behavior and system performance.

An overview of simulating real-world scenarios and testing AI systems to validate behavior, reliability, and edge cases before production.

A real-world case study showcasing the design, deployment, and optimization of an AI-powered invoice processing agent for enterprise use.

An overview of fine-tuning agent logic, prompts, and parameters to achieve more accurate, efficient, and predictable AI behavior.

A comprehensive review of the final project, evaluating architecture, design decisions, and real-world readiness of the agentic AI solution.

Guided assistance and mentorship to help build, refine, and successfully complete a production-ready capstone project.

An overview of deploying the final capstone project to a cloud platform with scalability, security, and production readiness

A final validation phase focused on monitoring system behavior and executing end-to-end tests to ensure production readiness.

The first presentation round where learners showcase capstone concepts, architecture, and initial implementation progress.

The final presentation round highlighting completed capstone solutions, live demos, and overall project outcomes.

A closing session celebrating project completion, key learnings, and next steps in the agentic AI journey.