Learn to design the next generation of AI systems. Explore the architectures and strategies behind autonomous agents that solve complex, real-world problems.
4.5
41 Lessons
2 Breakout Sessions
6h
Updated yesterday
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of AI agents and how they differ from AI models
- The ability to identify and explain core AI agent components and memory systems
- An explanation of different agent orchestration patterns and how to choose among them
- Hands-on experience designing and implementing AI agent safety guardrails
- Knowledge of integrating human oversight into agent workflows
- Understanding and applying strategies to overcome challenges in agentic systems
- Hands-on experience deconstructing real-world AI agent case studies
- Understanding the design and architecture of adaptive and robust AI agent systems
Learning Roadmap
1.
Agent Design Fundamentals
Agent Design Fundamentals
Learn core AI agent components, architecture, and how they perceive, reason, and act. Master orchestration, safety, and key design challenges.
2.
Multi-Agent Conversational Recommender System (MACRS)
Multi-Agent Conversational Recommender System (MACRS)
Explore MACRS, a multi-agent system for goal-directed conversational recommendations. See how it plans, uses reflection, and achieves superior performance.
3.
Nvidia Eureka Learning Agent
Nvidia Eureka Learning Agent
6 Lessons
6 Lessons
Dive into Eureka, an LLM-powered agent that autonomously designs and refines RL reward functions.
4.
Implementing a Eureka-Like Reward Learning Agent with Google ADK
Implementing a Eureka-Like Reward Learning Agent with Google ADK
5 Lessons
5 Lessons
Implement a Eureka-like reward learning system using ADK: generate, evaluate, select, reflect, and iterate reward functions end-to-end.
6.
Designing an AI Agent for Generating LLM Pipelines
Designing an AI Agent for Generating LLM Pipelines
4 Lessons
4 Lessons
Explore ChainBuddy’s innovative solutions for efficient LLM evaluation and workflow generation.
7.
Designing a Web Agent
Designing a Web Agent
5 Lessons
5 Lessons
Explore the development of advanced multimodal web agents for enhanced task performance.
8.
Designing a Multimodal-LLM Agent for Multi-Object Diffusion
Designing a Multimodal-LLM Agent for Multi-Object Diffusion
4 Lessons
4 Lessons
Explore MuLan's innovative approach to enhancing text-to-image generation through interactive, multi-step processes.
12.
Appendix: Free Reference Guides and Cheatsheets
Appendix: Free Reference Guides and Cheatsheets
3 Lessons
3 Lessons
A consolidated reference section covering core terminologies, architecture cheatsheets, and real-world application frameworks for AI agent design.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
This course offers a comprehensive overview of understanding and designing AI agent systems powered by large language models (LLMs). You’ll explore core AI agent components, delve into diverse architectural patterns, discuss critical safety measures, and examine real-world AI applications. You’ll learn to deal with associated challenges in agentic system design.
You will study real-world examples, including the Multi-Agent Conversational Recommender System (MACRS), NVIDIA’s Eureka for reward generation, and advanced agents navigating live websites and creating complex images. Drawing on insights from industry deployments and cutting-edge research, you will gain the foundational knowledge to confidently start designing your agent-based systems. This course is ideal for anyone looking to build smarter and more adaptive AI systems powered by LLMs.
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Evan Dunbar
ML Engineer
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Software Developer
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Souvik Kundu
Front-end Developer
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Vinay Krishnaiah
Software Developer
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