• views: 184
• date: 7 April 2026
• posted by: voska89

What you'll learn

Understand how Agentic AI systems work (agents, reasoning, tools, memory)
Build single-agent systems with tool usage and retrieval-based memory
Design and implement multi-agent architectures for complex workflows
Use frameworks like LangGraph, CrewAI, and AutoGen for orchestration
Integrate RAG (Retrieval-Augmented Generation) for grounded, accurate responses
Connect agents to real-world systems via APIs and automation tools (Zapier, n8n)
Implement evaluation pipelines (LLM-as-a-Judge, test cases, regression testing)
Add guardrails and safety mechanisms to prevent failures and misuse
Monitor systems with logging, observability, and performance tracking
Design scalable, production-ready AI architectures for enterprise use cases

Requirements

Basic understanding of AI concepts (helpful but not required)
Familiarity with Python is useful, but not mandatory
Comfortable using web apps and APIs (beginner level is enough)
Laptop with internet connection (Mac/Windows/Linux)
Ability to install tools like: Python and VS Code (or any IDE)
Willingness to learn hands-on by building real systems
No prior experience with agents or LLM frameworks required
Curiosity about AI, automation, and real-world applications

Description

"This course contains the use of artificial intelligence"
Master Agentic AI: From Foundations to Production-Ready Systems
This intensive 3-day course is designed to take you from understanding the basics of Agentic AI to building production-ready multi-agent systems that can operate in real-world business environments. You will go beyond simple prompting and learn how to design intelligent systems that can reason, act, use tools, and collaborate to complete complex tasks.
On Day 1, you will build a strong foundation in how AI agents actually work. You will learn the difference between chatbots, workflows, and agents, and understand core concepts like the ReAct loop (Think → Act → Observe). You will then dive into the anatomy of an AI agent, including system prompts, memory, tools, and output structures. By the end of the day, you will build your first working agent with tool integration and retrieval-based memory.
Day 2 focuses on scaling from single agents to multi-agent systems. You will explore powerful design patterns such as Planner–Executor, Researcher–Writer, and Critic–Improver, and understand why single agents fail in real-world scenarios. You will learn how to use modern orchestration frameworks like LangGraph, CrewAI, and AutoGen to design structured workflows. Through hands-on implementation, you will build a multi-agent system that collaborates across roles to solve end-to-end tasks.
On Day 3, you will transition from prototypes to production-ready AI systems. You will learn how to implement evaluation pipelines, including LLM-as-a-Judge, test cases, and regression testing. You will design guardrails and safety systems to prevent prompt injection, unsafe outputs, and tool misuse. You will also explore observability and monitoring, tracking inputs, outputs, latency, and failures. Finally, you will learn how to scale agent systems using cost optimization, parallelization, and caching, and design enterprise-grade architectures with API layers, orchestration layers, and governance.
Throughout the course, you will also dive into advanced topics like Agent + RAG (Retrieval-Augmented Generation) for knowledge grounding, Agent + automation (Zapier, n8n) for workflow execution, Agent + enterprise APIs for real-world integration, and Agent + UI (Streamlit) for building interactive applications.
The course culminates in a capstone project, where you will build a production-ready multi-agent system with tools, memory, evaluation, and guardrails—complete with a working demo and business use case.
By the end, you will not just understand Agentic AI—you will be able to design, build, and deploy scalable, reliable, and enterprise-ready AI systems that deliver real impact.

Who this course is for

Professionals looking to build real-world AI systems, not just use prompts
Software engineers who want to learn agentic AI and multi-agent architectures
Product managers aiming to design AI-powered products and workflows
Data scientists transitioning into LLM-based and agent-based systems
Entrepreneurs and builders creating AI startups or automation tools
Automation and no-code users wanting to upgrade to intelligent AI systems
Technical leaders exploring enterprise AI architecture and strategy
Anyone interested in moving from AI experimentation → production-ready systems




   
   
   

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