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Agentic System Design Crash Course
Short on time? Learn how to design real-world AI agents, analyze agentic architectures, and apply proven design frameworks through focused, high-impact lessons in just 8 hours.
4.6
11 Lessons
4h
Updated 1 week ago
Join 3 million developers at
Join 3 million developers at
LEARNING OBJECTIVES
- A structured framework for analyzing and designing agentic AI systems
- How to decompose complex AI problems into agent-based architectures
- How to integrate models, tools, memory, and control loops in autonomous agents
- How to design and evaluate multi-agent and multimodal systems
- How to improve agent performance through iterative reflection and feedback
- How to assess trade-offs in autonomy, safety, and scalability
- How to move from research case studies to implementation-ready designs
Learning Roadmap
1.
Agent Design Fundamentals
Agent Design Fundamentals
Understand core agent components, control loops, memory, orchestration patterns, and guardrails behind modern AI systems.
2.
Designing Real-World Agentic Systems
Designing Real-World Agentic Systems
Apply a structured framework to analyze and design real-world agentic systems across multiple domains.
How to Analyze Agentic Systems Using a Structured FrameworkDesigning Multimodal Web AgentsThought Exercise: Design a Self-Improving Web AgentDesigning a Multi-Agent Conversational Recommender SystemDesigning an Autonomous Reward Learning AgentImplementing an Autonomous Reward Learning Agent with Google ADKDesigning a Multimodal-LLM Agent for Multi-Object Diffusion
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
If you want a concise overview of how modern AI agents are designed, without going through a full-length course, “Agentic System Design Crash Course” covers the core architectural principles, design frameworks, and selected case studies from the main course in a condensed format.
You’ll learn how to design autonomous AI systems by understanding core agent components, orchestration patterns, memory and reflection mechanisms, safety guardrails, and evaluation strategies. Then, you’ll apply a structured design framework to analyze real-world agentic systems, including multi-agent recommenders, autonomous reward learning agents, multimodal web agents, and generative multimodal systems.
This course preserves the intellectual depth and architectural rigor of the full program while streamlining the learning path for experienced engineers and AI practitioners who want concentrated, high-value insights.
Trusted by 3 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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