Agentic AI Mastery Multi– Agent Systems in Practice

Build production-ready multi-agent AI systems with orchestration, tools, memory, and deployment in 3 days

What you'll learn

✓ Design and build multi-agent AI systems with specialized agent roles
✓ Implement agent orchestration workflows (Planner–Worker, Manager–Executor patterns)
✓ Integrate tools, APIs, and RAG-based memory into agent systems
✓ Develop production-ready architectures with FastAPI and simple UIs
✓ Apply guardrails, evaluation, and monitoring for reliable AI systems
✓ Optimize systems using parallel execution, caching, and cost control

Requirements

● Basic understanding of AI/LLMs (helpful but not required)
● Beginner-level Python knowledge (variables, functions, basic scripts)
● Familiarity with using tools like ChatGPT or Claude
● A computer capable of running Python (Mac, Windows, or Linux)
● Internet connection for accessing APIs and tools
● Willingness to build hands-on projects and experiment

Description

"This course contains the use of artificial intelligence"
The future of AI is no longer about single prompts—it's about building multi-agent systems that can plan, execute, collaborate, and deliver real outcomes.
In Agentic AI Mastery: Multi-Agent Systems in Practice, you will learn how to move beyond basic LLM usage and start building production-ready AI systems that mirror how real companies are deploying AI today.
Most courses focus on prompt engineering. This course focuses on agentic AI architecture—how to design systems where multiple specialized agents work together to solve complex problems.

You'll learn how to

structure Planner–Worker workflows, implement agent orchestration, and build systems that scale beyond simple tasks.
This is a highly hands-on program. You won't just learn concepts—you will build real systems. Starting with a single agent, you'll progress to multi-agent architectures with clearly defined roles, structured communication, and coordinated execution. You'll implement tool usage, integrate APIs, and add memory layers using techniques like RAG (Retrieval-Augmented Generation) to enable context-aware reasoning.
You'll also explore modern frameworks such as LangGraph, CrewAI, and AutoGen, while understanding when to use frameworks versus building your own orchestration layer. This ensures you gain both practical skills and architectural thinking.
Beyond building, you'll learn what it takes to make systems production-ready. You'll implement guardrails to control hallucinations and prevent prompt injection, design evaluation pipelines using metrics like task success and output quality, and add observability and monitoring to track system behavior, latency, and cost.
Deployment is a core part of this course. You will expose your system through a FastAPI backend, build a simple interface using Streamlit, and understand how to scale systems using async workflows, queue-based architectures, and caching strategies. You'll also learn how to optimize performance and reduce costs using token management and efficient system design.
By the end of the program, you will build a production-style AI company system—a portfolio-ready project with multiple agents, orchestration, memory, monitoring, and API access. This is the kind of system that reflects how AI is actually being used in enterprise environments.
This course is designed for builders, engineers, product leaders, and anyone serious about mastering agentic AI systems. If you want to move from simple LLM usage to designing scalable AI architectures, this program will give you the skills to do it.
The shift is already happening—from prompts to systems, from tools to AI-powered teams.
This course helps you stay ahead of that shift.

Who this course is for

■ Developers and engineers who want to build real-world AI systems, not just prompts
■ Product managers and tech leaders exploring agentic AI and automation strategies
■ AI enthusiasts looking to move from theory to hands-on system building
■ Founders and builders creating AI-powered products or startups
■ Professionals aiming to future-proof their careers with multi-agent system skills
■ Anyone serious about going from LLMs → Agent Systems → Production AI

Homepage

https://www.udemy.com/course/agentic-ai-mastery-multi-agent-systems-in-practice

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