Agentic AI Architectures with Patterns, Frameworks and MCP

Are you ready to make the transition from a traditional developer to an Agentic Architect?

We are moving from the era of simple Generative AI—where we passively ask models to write text—to the era of Agentic AI, where we design autonomous systems that can perceive, reason, act, and solve complex problems on their own.

Building a demo agent is easy. Building a scalable, secure, Enterprise-Grade System of Agents is hard.

This course is the comprehensive design guide for software architects and senior engineers who want to master of building real-world Agentic Architecture. This course goes beyond basic scripting; we focus on the patterns, protocols, and lifecycles required to integrate intelligent agents into massive enterprise ecosystems.

What You Will Learn

• Master Agentic Foundations: Distinguish between simple AI Agents and true Agentic AI, and master the "Agentic Loop" (Perception, Reasoning, Action, Learning).

• Architect Multi-Agent Systems: Learn how to orchestrate teams of agents to solve complex goals using collaboration patterns.

• Implement Agentic Design Patterns: Master reusable blueprints like ReAct, Reflection (Metacognition), and Planning to solve recurring architectural problems.

• Enterprise Integration with MCP: Dive deep into the Model Context Protocol (MCP) to build servers that expose your existing microservices as tools to any agent.

• Build Agentic RAG: Evolve traditional RAG into active research agents that can reason over your enterprise data.

• The Agent Development Lifecycle (ADLC): Manage the full lifecycle of an agent including security, governance, evaluation, and operations.

• Real-World Capstone Project: Design and integrate a complete Agentic Layer on top of a real-world E-Shop Microservices architecture.

Course Roadmap

This course follows a structured four-part journey designed to take you from foundational concepts to advanced enterprise implementation:

Part 1: The Foundations of the Agent

We start by answering the fundamental question: How do you build an AI Agent? We will dissect the anatomy of an agent and distinguish between simple tools and autonomous systems. You will master the heartbeat of every agent—The Agentic Loop—tracing the journey of data through Perception, Reasoning, Action, and Learning to understand the agent's internal physiology.

Part 2: Architecture, Patterns & Frameworks

Once we understand the agent, we scale up to the system level. We will explore Agentic Architecture, specifically focusing on Multi-Agent Collaboration and Agentic Workflows. You will compare architectural styles like Orchestration vs. Choreography and survey the landscape of top frameworks (LangChain, LlamaIndex, Microsoft Agent Framework). Critically, we dedicate a massive section to Agentic Design Patterns, where you will learn to implement:

• The Planning Pattern: Decomposing complex goals.

• The Reflection Pattern: Enabling agents to critique and improve their own work.

• The ReAct Compound Pattern: Combining reasoning and action loops.

Part 3: Enterprise Integration & Advanced Techniques

Here we move to the integration layer. We will tackle the biggest challenge in the industry: Agent Protocols. You will get a step-by-step guide to the Model Context Protocol (MCP), learning how to build servers that standardize how agents talk to your APIs. We will also cover Agentic RAG for advanced reasoning over data and Context Engineering to replace brittle prompt engineering with robust information architecture.

Part 4: Lifecycle & Capstone Project

Finally, we cover the Agent Development Lifecycle (ADLC), addressing hard enterprise requirements like security, governance, and evaluation. We conclude with a massive Capstone Project where you will take everything you've learned to design and integrate a multi-agent system into a real-world E-Shop Microservices backend.

Who This Course Is For:

• Software Architects looking to integrate AI agents into existing enterprise architectures.

• Senior Developers wanting to move beyond basic LLM prompting to build autonomous systems.

• AI Engineers seeking to master standard protocols like MCP and advanced multi-agent patterns.

• Technical Leads responsible for defining the strategy for their company's AI adoption.

By the end of this course, you will not just be able to write an agent script. You will be able to architect, integrate, and manage entire agentic systems for the enterprise.