Lesson 04: Designing AI Systems with Agent, System, and Data Views
In this lesson, you will learn how to design AI systems by viewing them through three essential perspectives: the agent (user) view, the system (team) view, and the data (payload) view.
You will explore how AI agents interact with users through natural language, how systems orchestrate workflows behind the scenes, and how structured data enables reliable communication between agents and tools. This lesson bridges the gap between human conversation and machine execution.
Using real-world examples from websites, restaurants, travel booking, and food services, this lesson shows how clear separation of views leads to better user experience, smoother operations, and scalable AI architectures.
What You Will Learn
By the end of this lesson, you will be able to:
-
Understand the three core views of any AI system: agent, system, and data
-
Explain how the agent (user) view handles perception, intent, and conversation
-
Understand how the system view manages roles, prompts, workflows, and handoffs
-
Recognize how the data view structures information using formats like JSON
-
Identify how poor separation leads to confusion and system failures
-
Design AI solutions that are easier to debug, scale, and maintain
Why This Lesson Is Important
AI systems fail not because of intelligence, but because of unclear design.
When user interaction, system logic, and data are mixed together, AI becomes difficult to control and unpredictable to maintain. The three-camera-angle approach gives you clarity by separating concerns just like how real organizations separate customer service, operations, and records.
This lesson helps you design AI systems that feel human on the surface, reliable behind the scenes, and consistent in how data flows across agents and tools.
Practical Exercise
In this lesson, you will apply the three-view framework by:
-
Analyzing a real-world scenario such as booking travel or ordering catering
-
Identifying the agent (user-facing) interactions
-
Mapping the system-level responsibilities and agent handoffs
-
Structuring the underlying data required to support decisions
-
Reviewing how all three views work together as a cohesive system
You will also see how these views apply to both single-agent and multi-agent systems.
No advanced technical background is required.
Busy schedule? Learn anytime, at your own pace.
Get full access to the complete Agentic AI bootcamp as a flexible, self‑paced on‑demand course, designed for learners who cannot join live sessions but still want the same depth and projects.
What’s included
-
Full session recordings so you can watch every lesson, demo, and Q&A on your own schedule.
-
Downloadable lab instructions and templates to guide you step‑by‑step through all hands‑on exercises and projects.
-
Lab support channel where you can ask questions, share screenshots, and get help if you are stuck on any exercise.
-
Same projects and portfolio artifacts as the live cohort, so your outcomes and skills are aligned.
Pricing
The on‑demand course is offered at the same price as the live Agentic AI bootcamp and includes all future updates to the recordings and labs for that cohort.