Professional Certificate Program in Agentic AI & Multi-Agent Systems

• Python programming essentials for AI and machine learning applications
• Environment setup across IDEs and cloud platforms
• Data structures, control flow, and object-oriented programming basics
• File handling and coding workflows for data tasks
• AI-powered code generation using GitHub Copilot
• Hands-on exercises comparing traditional and AI-assisted coding approaches
   

• Learn, Build, Deploy framework for AI systems
• AI hierarchy, including AI, ML, DL, GenAI, and Agentic AI
• Transformer architectures and autonomous AI agents
• Key research such as Attention Is All You Need
• Prompting techniques, including Chain-of-Thought and ReAct
• Four-layer generative AI stack model

• Four-layer GenAI stack: Infrastructure, Model, Orchestration, Application
• Cloud platforms and vector databases supporting GenAI systems
• Foundation models and model tuning approaches
• Agent frameworks and workflow orchestration
• Prompt engineering techniques, including zero-shot, CoT, and ReAct
• Hands-on demos for prompt design and experimentation

• Planning systems for AI-assisted workflows
• Prompt engineering principles for LLM interaction
• Building Q&A bots using LangChain and function-calling APIs
• Integration of external APIs and services in AI workflows
• Agent-driven planning workflows
• Development of multi-step agents with contextual tool integration

• RAG systems and multi-agent architectures using CrewAI and LangGraph
• Agent collaboration patterns and role-based architectures
• YAML-based agent role design
• Memory strategies for multi-agent systems
• Orchestration frameworks for agent collaboration
• Modular agent teams for scalable information synthesis

• Enterprise agent orchestration using AutoGen and n8n automation
• Communication protocols within multi-agent ecosystems
• Database integration within agent workflows
• Marketing agent pipeline projects
• Scalability, performance, security, and compliance considerations
• Visual workflow design and distributed agent system concepts

• Model Context Protocol (MCP) for AI tool integration
• Structured context binding and interoperability standards
• JSON schema design for tool communication
• Secure tool hosting and authentication methods
• Chaining tools and agents across workflows
• Memory persistence and enterprise security considerations

• AI agent performance evaluation using OKRs and key indicators
• Metrics such as success rate and latency
• Observability tools, including LangSmith and Phoenix
• Logging and conversational analysis techniques
• Pricing strategy and go-to-market planning
• Deployment of agent MVPs with analytics dashboards

• UX design patterns for agentic AI systems
• Interaction design for probabilistic workflows
• Handling ambiguity in AI interactions
• Human-in-the-loop checkpoints
• Managing hallucination and bias risks
• Transparency through confidence signals and explainability

• Cloud vs edge hosting strategies
• Serverless and containerized environments
• Model hosting approaches
• Automation workflows using Firebase and n8n
• Feedback systems and testing frameworks
• Infrastructure-as-code tools, including Terraform and Pulumi

• Azure infrastructure for AI agent development
• Azure frameworks and orchestration workflows
• Deployment pipelines for AI applications
• Security integration and compliance considerations
• Hosting AI agents in the Azure ecosystem
• Scalable orchestration within cloud environments

Apply multi-agent design and product strategy in a comprehensive capstone. Participants build a four-agent market research and GTM system using n8n and CrewAI with MCP integration. The project includes Lean Canvas planning, pricing strategy, performance tracking, and chatbot deployment.