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Building & Evaluating Agentic AI Systems
IntermediateLevel
233+Students Enrolled
6 Hrs Duration
About this Course
- In this course you are going to learn about building and evaluating agentic ai systems through advanced agent architecture patterns and orchestration strategies.
- Explore agentic AI trends and latest tools like Tavily Search API, Google AI Studio, and DeepEval to build intelligent, scalable, production-ready autonomous systems.
- Understand memory management, context engineering, few-shot prompting, and chain-of-thought prompting techniques for building conversational and reasoning-based agents.
- Master retrieval augmented generation, vector databases, and agentic AI examples including Text2SQL agents using LangChain, LangGraph, and FastMCP for enterprise applications.
Course Benefits
- Master the complete agentic AI learning path and gain expertise in agent architecture, types of AI agents, and AI agents vs agentic AI distinctions.
- Stay ahead with agentic AI trends knowledge and understand emerging patterns in agent architecture and autonomous AI systems.
- Learn cutting-edge tools including FastMCP, Tavily Search API, Google AI Studio, and comprehensive vector database management.
- Gain industry-recognized certification validating your expertise in building production-ready agentic AI systems at enterprise scale.
Learning Outcomes
Agent Architecture
Learn AI agent types and modern agent architectures.
Multi-Agent Systems
Build Text2SQL agents with vector database integration.
Production AI
Deploy AI agents with FastMCP, APIs, and monitoring.
Who Should Enroll
- AI Engineers & ML Pros: Master agentic AI, agent architecture, and production-ready AI systems.
- Python Developers: Learn different prompting technique, few-shot prompting, and chain-of-thought techniques.
- Data Scientists: Build AI agents with LangGraph, vector DBs, and agentic AI design patterns.
- Product & Tech Leaders: Design scalable AI agent systems with FastMCP and modern architectures.
Course Curriculum
A comprehensive 6-week agentic AI learning path covering agent architecture, types of AI agents, RAG systems with vector databases, agentic AI trends, Text2SQL agents, and advanced evaluation frameworks.
A comprehensive step-by-step setup guide covering platform configuration (Google Colab, ChatGPT, Claude, Google AI Studio), LLM API keys (OpenAI, Gemini, Groq), and essential development tools (LangChain, LangGraph, Tavily, LangSmith).
1. Setup Instructions Live Demo
Learn the fundamentals of Generative AI, LLMs, and Agentic AI, along with prompting and RAG techniques. Explore real-world applications through hands-on demos across leading platforms like ChatGPT and Claude. Gain practical exposure to APIs, tools, and environment setup for building and deploying AI-powered systems.
1. Introduction to Generative AI and Agentic AI
Learn prompt engineering fundamentals and advanced techniques like persona, few-shot, and chain-of-thought prompting. Explore multimodal capabilities across text, images, audio, and video with hands-on demos. Gain practical experience using LangChain to solve real-world problems, automate workflows, and build intelligent AI-driven applications.
1. Prompt Engineering and LangChain Essentials
Learn the foundations of Retrieval-Augmented Generation (RAG) by building an end-to-end pipeline from scratch. This session covers document ingestion, text chunking, embeddings, vector databases, retrievers, and answer generation, with hands-on guidance to create a document-grounded QA system that produces accurate and context-aware responses.
1. Build your first RAG Systems
Advance your RAG systems with techniques that improve retrieval quality and answer accuracy. Explore multi-query retrieval, keyword and hybrid search, reranking, contextual retrieval, and prompting strategies to reduce hallucinations. Apply these methods through practical projects to build robust, production-ready RAG applications.
1. Build Advanced RAG Systems
Learn the fundamentals of Agentic AI by understanding what makes systems agentic and how LLMs, tools, prompts, tool-calling, and orchestration work together. Build your first ReAct AI agent using LangChain and LangGraph with built-in utilities, gaining hands-on experience with the agent reasoning and action loop.
1. Build Your First ReAct AI Agent with LangChain & LangGraph
Build ReAct agents from scratch using LangGraph by working with states, nodes, edges, conditionals, and memory. Explore essential agentic design patterns such as routing, planning, reflection, and multi-agent workflows, then apply them to create scalable, real-world AI agents for complex problem-solving.
1. Agentic AI Design Patterns & Building ReAct Agents from Scratch with LangGraph
Learn how to build conversational AI agents that remain reliable across long, multi-turn interactions through effective context engineering. Explore short-term and long-term memory, history management, memory trimming, tool output summarization, and context retrieval techniques while building a memory-aware Healthcare Assistant that delivers consistent, relevant responses.
1. Memory Management & Conversational Agentic AI Systems
Discover how the Model Context Protocol (MCP) enables standardized integration between AI agents, tools, prompts, and data. Learn to build MCP servers and clients with FastMCP, understand multi-server MCP architectures, and explore how MCP supports scalable, enterprise-grade agentic systems across cross-functional workflows.
1. Integrating MCP with Agentic AI Systems
Meet the instructor
Our instructor and mentors carry years of experience in data industry
Get this Course Now
With this course you’ll get
- 6 Hours
Duration
- Alessandro Romano & + 1 More
Instructor
- Intermediate
Level
Certificate of completion
Earn a professional certificate upon course completion
- Industry-Recognized Credential
- Career Advancement Credential
- Shareable Achievement
Frequently Asked Questions
Looking for answers to other questions?
AI agents are systems that perceive and act within an environment. Agentic AI refers to AI systems with autonomous decision-making capabilities. The key difference: AI agents follow programmed rules, while agentic AI systems can reason, plan, and adapt dynamically. This course teaches you the complete agentic AI learning path.
Agent architecture refers to the structural design and components of an AI agent system (perception, reasoning, action modules). Types of AI agents include reactive agents, deliberative agents, hybrid agents, and multi-agent systems. Understanding agent architecture helps you design appropriate types of AI agents for specific problems.
You'll build agentic AI examples including Text2SQL agents that query databases, financial intelligence agents, healthcare provider discovery agents, invoice processing agents, and customer support routers. These demonstrate different types of AI agents and agent architecture patterns.
Key agentic AI trends include multi-agent orchestration, advanced memory management, tool use evolution, FastMCP protocols, and integration with vector databases. This course covers cutting-edge agentic AI trends and prepares you for emerging patterns in autonomous AI systems.
Few-shot prompting provides examples to guide agent behavior with minimal training data. Chain-of-thought prompting helps agents reason step-by-step through complex problems. Both techniques enhance types of AI agents by improving reasoning capabilities and reducing hallucinations.
Week 1-2 content is available now covering Generative AI and RAG systems. In the upcoming week, you'll receive Week 3 & 4 content focusing on building agentic AI systems and agent architecture. The following week, Week 5 & 6 content covering advanced multi-agent systems and evaluation frameworks will be released. After completing all the weeks of content, you will be able to access your completion certificate.
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