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Agentic System Design

Learn to design the next generation of AI systems. Explore the architectures and strategies behind autonomous agents that solve complex, real-world problems.
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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.
This course offers a comprehensive overview of understanding and designing AI agent systems powered by large language models (LL...Show More

WHAT YOU'LL LEARN

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
An understanding of AI agents and how they differ from AI models

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Content

2.

Multi-Agent Conversational Recommender System (MACRS)

4 Lessons

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

6 Lessons

Dive into Eureka, an LLM-powered agent that autonomously designs and refines RL reward functions. Learn about its evolutionary search and human feedback integra

4.

Applying Agentic Design Principles

1 Lessons

5.

Designing an AI Agent for Generating LLM Pipelines

4 Lessons

Explore ChainBuddy’s innovative solutions for efficient LLM evaluation and workflow generation.

6.

Designing a Web Agent

5 Lessons

Explore the development of advanced multimodal web agents for enhanced task performance.

7.

Designing a Multimodal-LLM Agent for Multi-Object Diffusion

4 Lessons

Explore MuLan's innovative approach to enhancing text-to-image generation through interactive, multi-step processes.

8.

Thought Exercise: AI Hospital

1 Lessons

Design a self-improving multi-agent system for enhanced medical diagnosis.

9.

Wrapping up

1 Lessons

Certificate of Completion
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Developed by MAANG Engineers
Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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Build prompt engineering skills. Practice implementing AI-informed solutions.

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Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

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AI Code Mentor

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