Agentic AI Architectures with Patterns, FrameWorks and MCP

Agentic AI Architectures with Patterns, Frameworks and MCP
Published 12/2025
Duration: 4h 8m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 2.40 GB

Design and Integrate Multi-Agent systems into Enterprise Apps using AI Agents, Agentic Workflows, Design Patterns & MCP

What you’ll learn
– What are AI Agents ?
– What are the Components of AI agents? How to Build an AI Agent ?
– What is Agentic AI ? Multi-Agent Architectures
– What are Agentic Workflows ?
– AI Agent Frameworks – Exploring AI Agentic Frameworks
– Understanding AI Agentic Design Patterns
– What is Agentic RAG ? – Advanced Reasoning: Agentic RAG
– Agent Communications and Protocols – Using Agentic Protocols (MCP, A2A and ACP)
– Model Context Protocol (MCP) in Action Design
– Context Engineering for AI Agents
– The Agent Development Lifecycle (ADLC) w/ Enterprise Agent Development
– Building the E-Shop Agentic Layer
– How AI Agents Uses Generative AI ?
– When to Use AI Agents (And When Not To)
– The Agentic Loop: Perception, Reasoning, Action, Learning (PRAL)
– AI Agent vs. Agentic AI: The Critical Difference
– How Agentic AI Works: System-Level Architectures (Orchestration or Choreography)
– Agentic Orchestration Patterns (Sequential, Concurrent, Group chat, Handoff , Magentic)
– The "Tool Use" Pattern
– The "Planning" Pattern
– The "Reflection" Pattern (Metacognition)
– The "ReAct" Compound Pattern
– The "Router & Specialist" Pattern
– The "Handoff" Pattern (Sequential Workflow)
– The "Group Chat / Debate" Pattern
– The "Swarm" Pattern (Parallelization)
– The "Human-in-the-Loop" (HITL) Pattern

Requirements
– Basics of Software Architecture
– Basics of Generative AI

Description
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 ofAgentic 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 Agentsis hard.

This course is thecomprehensive design guideforsoftware architectsandsenior engineerswho 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 likeReAct,Reflection(Metacognition), andPlanningto solve recurring architectural problems.

Enterprise Integration with MCP:Dive deep into theModel 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-worldE-Shop Microservicesarchitecture.

Course Roadmap

Once we understand the agent, we scale up to the system level. We will exploreAgentic Architecture, specifically focusing onMulti-Agent CollaborationandAgentic 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 toAgentic 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.
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 theModel Context Protocol (MCP), learning how to build servers that standardize how agents talk to your APIs. We will also coverAgentic RAGfor advanced reasoning over data andContext Engineeringto replace brittle prompt engineering with robust information architecture.

Finally, we cover theAgent Development Lifecycle (ADLC), addressing hard enterprise requirements like security, governance, and evaluation. We conclude with a massiveCapstone Projectwhere you will take everything you’ve learned to design and integrate a multi-agent system into a real-worldE-Shop Microservicesbackend.

Who This Course Is For:

Software Architectslooking to integrate AI agents into existing enterprise architectures.

Senior Developerswanting to move beyond basic LLM prompting to build autonomous systems.

AI Engineersseeking to master standard protocols like MCP and advanced multi-agent patterns.

Technical Leadsresponsible 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 toarchitect, integrate, and manage entire agentic systems for the enterprise.

Who this course is for:
– Software Developers & Architects who wants to master Agentic AI Architectures
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