This course empowers architects and technical leaders to design and build the next generation of intelligent systems. Moving beyond the excitement of large language models and LLM-powered chatbots, you'll learn to architect cohesive, scalable, and resilient AI Systems, which include AI agents, agentic AI, and the emerging concept of compound AI systems.

This course explores the different types of AI systems where their complexity and architecture patterns differ dramatically. Leveraging the AIscape Framework, you'll gain a roadmap for understanding the evolution of AI systems and the architectural patterns that drive them.

You'll master proven architectural patterns, decision-making frameworks, and practical considerations for designing modular AI solutions that deliver significant business value and adapt to future technological advancements. You'll also learn to understand which architecture artifacts are essential for the different AI systems.

The course explores the AIscape (i.e., AI landscape) framework, which identifies seven types of AI systems, with AGI being identified as a future type, Type 8:

Type 0 - Rule-Based / Statistical Systems

Type 1 - Traditional ML Systems

Type 2 - Deep Learning Systems

Type 3 - Generative & Foundational Models

Type 4 - Tool-Using LLM Systems

Type 5 - Agentic AI Systems

Type 6 - Compound AI Systems

Type 7 - Cognitive AI Ecosystems

The architecture views required per AI system type will be explored along with the relevant patterns.

Beyond system classification, the AIscape Framework serves as a comprehensive skills development roadmap for AI professionals. Whether you're a data scientist looking to advance from Type 1 traditional ML to Type 4 RAG systems, or a software engineer planning progression toward Type 6 compound AI expertise, AIscape provides clear learning paths that align individual skill development with organizational AI evolution.

Architectural thinking is the paramount skill being developed to move the discussion away from bias and preferences to systematic architectural reasoning. For example, the active debate on whether to choose between single-agent and multi-agent AI systems represents one of the most important architectural decisions in modern AI Agent deployment, with significant implications for complexity, performance, and maintainability. But there is no universally right or wrong answer; it's an architectural decision that should be grounded in the type of AI system (hence the use of AIscape) being built, along with its business objectives.

Rather than calling LLMs a new computing paradigm, we discuss the architectural framing of LLMs as a new software substrate for building language-native applications. They represent a new interface to computing—making it possible to interact with systems via natural language.

Your Transformation

By the end of this course, you'll transform from someone who thinks about AI tools and models in isolation to a strategic architect who designs integrated AI ecosystems. You'll gain the confidence to assess any AI initiative using the AIscape Framework, recommend the appropriate architectural patterns, and create the documentation needed to guide successful implementations. Most importantly, you'll develop the architectural mindset that separates strategic technology leaders from tactical implementers—enabling you to shape your organization's AI future rather than simply react to AI trends.

Whether you're planning your organization's first AI system or evolving toward sophisticated compound AI solutions, you'll leave with a proven framework, practical patterns, and the architectural thinking skills to build AI systems that scale, adapt, and deliver lasting business value.

Additionally, you'll gain a personal AI skills roadmap using AIscape to identify which competencies to develop next in your AI career journey, whether advancing from traditional ML expertise to agentic AI systems or building toward compound AI architecture mastery.