Refresh Python fundamentals for AI/ML use cases, including core syntax, IDE and cloud setup, data structures, control flow, OOP basics, file operations, and AI-assisted coding with GitHub Copilot. Practice real-world exercises and complete a mini capstone comparing manual and AI-supported coding.

Explore the Learn > Build > Deploy approach while understanding AI categories such as ML, DL, GenAI, and agentic AI. Learn transformer fundamentals, autonomous agent concepts, and key research like Attention is All You Need. The course also covers CoT and ReAct prompting and a layered GenAI stack model.

Study the four-layer GenAI stack—Infrastructure, Model, Orchestration, and Application—with focus on scalability, cost, and lifecycle decisions. Topics include cloud and vector systems, foundation models and tuning, agent workflows, and low-code prototyping. Build prompt engineering skills using zero-shot, CoT, and ReAct methods.

Learn how planning systems improve AI productivity using LangChain and function calling APIs. Build Q&A bots, connect external APIs, and design agent-driven workflows through live sessions and labs. Strengthen prompt strategies while developing multi-step agents with contextual tool integration.

Build advanced multi-agent and RAG systems through hands-on work with CrewAI and LangGraph. Learn agent collaboration patterns, YAML-based role design, memory approaches, and orchestration techniques. Develop modular agent teams that support scalability, state handling, and autonomous synthesis, supported by pitch-based deliverables.

Advance into enterprise agent orchestration using Microsoft AutoGen and n8n automation. Cover multi-agent communication protocols, database integration, and deployment considerations. Work on projects such as marketing agent pipelines while addressing scalability, performance, security, and compliance through workflow-driven implementation.

Understand how the Model Context Protocol (MCP) enables standardized tool integration. Learn structured context binding, interoperability principles, JSON schema design, secure tool hosting, and persistent memory. Labs focus on chaining tools with authentication and optimizing performance for enterprise-grade deployments.

Develop frameworks to evaluate AI agent performance using OKRs, success rates, latency, and ROI metrics. Learn observability and tracing with LangSmith and Phoenix, supported by logging and conversation analysis. The course also covers pricing, go-to-market strategy, and deploying agent MVPs with analytics dashboards.

Focus on designing trustworthy user experiences for agentic AI products. Learn interaction patterns for probabilistic flows, ambiguity handling, and human-in-the-loop checkpoints. Explore risk mitigation for hallucinations and bias using guardrails, confidence disclosures, and explainability techniques while building UX prototypes centered on control and transparency.

Prepare AI products for deployment and operations by exploring cloud vs edge hosting, serverless options, containers, and model hosting approaches. Work with Firebase and n8n workflows, feedback and testing systems, monitoring alerts, and infrastructure-as-code basics using Terraform and Pulumi to support scalable product readiness.

Learn to build and deploy AI agents using Microsoft Azure services. The course covers Azure-specific tooling, orchestration workflows, security integration, and scalable deployment practices. Gain practical experience hosting AI agents in the Azure ecosystem while addressing enterprise compliance and reliability requirements.

Apply multi-agent system design and GTM strategy in a production-grade capstone. Build a four-agent market research and GTM workflow using n8n and CrewAI with MCP integration. The project includes Lean Canvas planning, pricing and acquisition strategy, performance instrumentation, and chatbot deployment to simulate real AI product leadership.