Applied Agentic AI: Systems, Design & Impact

This module revisits Python programming essentials tailored for AI/ML applications. It covers core constructs, environment setup across IDEs and cloud platforms, data structures, control flow, OOP basics, file handling, and AI-powered code generation with GitHub Copilot. Hands-on exercises focus on real-world data tasks and culminate in a capstone comparing traditional and AI-assisted coding. Learners also earn Microsoft Learn badges on the Microsoft Learn portal for Microsoft-branded courses.

This course introduces the Learn > Build > Deploy framework and covers AI hierarchy distinctions (AI, ML, DL, GenAI, Agentic AI), transformer architectures, and autonomous AI agents. Topics include key papers like "Attention is All You Need," CoT prompting, ReAct frameworks, and a 4-layer GenAI stack analogy. It establishes foundational knowledge critical to technical product management of AI systems, with an emphasis on theoretical and applied agentic AI concepts.

This deep dive explores the 4-layer GenAI technology stack—Infrastructure, Model, Orchestration, and Application—with emphasis on scalability, cost, and product lifecycle management. It covers cloud platforms and vector databases, foundation models and fine-tuning, agent frameworks and workflows, and low-code prototyping with UX design principles. The course also builds prompt engineering mastery using zero-shot, CoT, and ReAct techniques through hands-on demos.

Focusing on PM productivity with AI, this module teaches prompt engineering principles and planning systems using LangChain and function calling APIs. It includes live sessions building Q&A bots integrating APIs, planning workflows with agents, and advanced prompt strategies to optimize interaction with language models. Labs guide the development of multi-step agents and contextual tool integration, enhancing practical skills in agent-based product development.

This course explores advanced retrieval-augmented generation (RAG) systems and multi-agent architectures through hands-on implementation with CrewAI and LangGraph. It details agent collaboration patterns, role-based architectures using YAML, memory strategies, and real-world orchestration frameworks. Learners build modular multi-agent teams focusing on scalability, state management, and autonomous information synthesis. Deliverables include pitches advocating modular agent architectures.

Building on foundational multi-agent knowledge, this course delves into enterprise-grade agent orchestration using Microsoft AutoGen and n8n workflow automation. Covered are communication protocols, database integration, and production deployment strategies. Projects include developing marketing agent pipelines with attention to scalability, performance, security, and compliance. Visual workflows and protocol deep dives support mastery of complex distributed agent ecosystems.

This module introduces the Model Context Protocol (MCP) for integrating and standardizing AI tools. Topics include structured context binding, interoperability standards, JSON schema design, secure tool hosting, and memory persistence. Labs develop contextual AI agents chaining outputs across tools with authentication and performance optimization. Emphasis is on enterprise readiness, security best practices, and tool discoverability through standardized protocols.

Offering a comprehensive framework, this course teaches measurement of AI agent performance using OKRs, key indicators like success rate and latency, and ROI calculations. It covers observability tooling with LangSmith and Phoenix, real-time logging, and conversational analysis. Business strategy topics include pricing, go-to-market planning, and deployment of agent MVPs with analytics dashboards. Practical instrumentation and monitoring equip learners for operational excellence.

Centering user experience for AI products, this module covers interaction design patterns for agentic UX, including flexible, probabilistic flows, ambiguity handling, and human-in-the-loop checkpoints. It addresses ethical risks such as hallucinations and bias and teaches guardrail implementations and transparency techniques like confidence disclosures and explainability interfaces. Learners create complete UX prototypes emphasizing trust, user control, and fail-soft design.

Focused on deployment and live operations, this course examines cloud vs edge hosting, serverless and containerized environments, and model hosting strategies. It includes hands-on Firebase and n8n automation workflows, feedback and testing system integrations for user insights, alert configurations for monitoring, and infrastructure-as-code introductions with Terraform and Pulumi. The course prepares learners for scalable, maintainable AI product readiness.

This course focuses on building AI agents leveraging Microsoft Azure’s cloud infrastructure and toolset. It covers Azure-specific frameworks, deployment workflows, security integration, and scalable orchestration techniques. Learners gain hands-on experience developing and hosting AI agents in the Azure ecosystem with attention to enterprise-grade reliability and compliance.

This masterclass provides in-depth exposure to real-world applications and advanced concepts in agentic AI systems. Industry experts share case studies, best practices, and emerging trends in building autonomous multi-agent architectures. Participants explore cutting-edge frameworks, protocols, and business models driving innovation in agentic AI deployments.