Module 1: introduction to AI applications and agents on Azure
This module introduces the evolution of AI applications, focusing on the transition from traditional models to generative and agent-based systems.
Topics include:
- Overview of AI application architectures
- Introduction to generative AI concepts
- Understanding agent-based systems and their role in modern AI
- Overview of Microsoft Foundry capabilities
- Azure services for AI development
Learning outcomes:
- Understand the key components of AI-powered applications
- Identify when to use generative AI versus agent-based approaches
- Describe the role of Microsoft Foundry in AI development
Module 2: developing generative AI applications
This module focuses on building applications powered by generative models using Azure and Microsoft Foundry.
Topics include:
- Working with foundation models
- Prompt engineering techniques
- Designing application workflows with generative AI
- Managing inputs, outputs, and context
- Evaluating and refining model responses
Learning outcomes:
- Build and test generative AI applications
- Apply prompt engineering to improve output quality
- Design workflows that incorporate generative models effectively
Module 3: building AI agents on Azure
This module explores how to design and implement AI agents that can perform tasks, make decisions, and interact with users and systems.
Topics include:
- Agent architecture and design patterns
- Task planning and execution
- Managing agent state and memory
- Event-driven and autonomous agent behaviours
- Debugging and monitoring agent performance
Learning outcomes:
- Design and implement AI agents for real-world scenarios
- Enable agents to plan and execute tasks
- Monitor and optimise agent behaviour
Module 4: integrating tools and knowledge into agentic solutions
This module focuses on enhancing AI agents by connecting them to external tools, APIs, and knowledge sources.
Topics include:
- Tool integration patterns for agents
- Connecting agents to APIs and external services
- Knowledge grounding and retrieval techniques
- Implementing retrieval-augmented generation (RAG)
- Managing data sources and context injection
Learning outcomes:
- Integrate tools and APIs into agent workflows
- Enable agents to access and use external knowledge
- Improve response accuracy through grounded AI techniques
Module 5: developing natural language AI solutions
This module covers techniques for building applications that understand and generate human language.
Topics include:
- Natural language processing fundamentals
- Conversational AI design
- Text analysis and classification
- Language generation and summarisation
- Building chat-based interfaces
Learning outcomes:
- Develop applications that process and generate natural language
- Design conversational experiences
- Apply NLP techniques to real-world use cases
Module 6: multimodal AI and complex content understanding
This module introduces multimodal AI, enabling applications to process and reason across different types of data.
Topics include:
- Working with image and text inputs
- Multimodal model capabilities
- Extracting insights from visual data
- Combining modalities in a single workflow
- Handling complex and unstructured content
Learning outcomes:
- Build applications that process both text and images
- Extract meaningful insights from visual data
- Design workflows that combine multiple data types
Module 7: building scalable AI solutions with Microsoft Foundry
This module focuses on deploying and scaling AI applications and agents in production environments.
Topics include:
- Application deployment strategies on Azure
- Scaling AI workloads
- Performance optimisation
- Monitoring and logging
- Security and responsible AI considerations
Learning outcomes:
- Deploy AI applications to production environments
- Optimise performance and scalability
- Implement monitoring and governance practices
Module 8: designing production-ready AI systems
This module brings together all course concepts to design robust, enterprise-ready AI solutions.
Topics include:
- End-to-end solution design
- Architectural best practices
- Managing lifecycle and updates
- Testing and validation strategies
- Real-world use case scenarios
Learning outcomes:
- Design complete AI systems from concept to deployment
- Apply best practices for reliability and maintainability
- Deliver scalable, production-ready AI solutions
Key benefits
- Official Microsoft-authored content aligned to current Azure AI capabilities
- Focus on modern generative AI and agent-based development patterns
- Hands-on approach to building real-world AI applications
- Coverage of multimodal AI and knowledge-integrated solutions
- Prepares learners to design and deploy production-ready AI systems
- Natural progression from AI-102 with updated, future-focused content
