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Agentic AI Engineer: The Complete AI Agents & MCP Course

Name: Agentic AI Engineer: The Complete AI Agents & MCP Course
Rating: 4.5 (17 reviews)

Build and Deploy AI Agents, Multi-Agent Systems and MCP Apps

Created byCorels Emmanuel

Last updated 10/2026

English

What you'll learn

Understand how AI agents reason, use tools, maintain context and complete multi- step objectives.
Distinguish autonomous agents from deterministic workflows and conventional software automation.
Build complete agent loops from scratch using Python and structured tool calling.
Apply chaining, routing, parallelization, delegation, handoffs and orchestrator- worker patterns.
Create dependable agents with structured outputs, validation, guardrails and human approval.
Build single-agent and multi-agent applications with OpenAI Agents SDK.
Develop coordinated agent crews with CrewAI, custom tools and hierarchical workflows.
Build stateful and resumable agent applications with LangChain and LangGraph.
Create browser-enabled agents with Playwright and MCP.
Build coding agents that safely execute and test software inside Docker.
Use AutoGen and Microsoft Agent Framework to create typed agents and multi- agent workflows.
Explore Google ADK, Pydantic AI, AWS Strands, Agno and Mastra.
Design agents that generate, test and launch other agent applications.
Connect AI agents to tools, data and external services through Model Context Protocol.
Build custom MCP servers with FastMCP.
Add short-term memory, long-term memory, document retrieval and agentic RAG.
Implement tracing, observability, automated evaluations and feedback loops.
Control model usage, execution cost, latency, permissions and tool access.
Build user interfaces with Gradio and APIs with FastAPI.
Package and deploy agent applications with Docker and React.
Select the right model, framework and architecture for different Agentic AI projects.
Complete eight portfolio-ready Agentic AI engineering projects.

Course content

32 sections • 368 lectures • 81h 23m total length

Stage 1: Lesson 1 : Welcome to the Agentic AI Engineer Course10:00
Introduce the course outcomes, teaching approach, project portfolio and
expectations for completing the program.
Stage 1 : Lesson 2: Build Your First Autonomous AI Workflow with n8n15:00
Stage 1 : Lesson 3: Course Roadmap—Six Modules, Thirty Stages and Eight Projects10:00
Stage 1: Lesson 4: Your Path to Becoming an Agentic AI Engineer10:00
Stage 1: Lesson 5: Understanding the Agentic AI Development Stack15:00
Stage 1: Lesson 6: How the Course Projects Connect to Real-World Applications10:00

Lesson 1: Environment Setup with Python, uv, Git and GitHub15:00
Lesson 2: Windows Development Environment Setup15:00
Lesson 3: macOS Development Environment Setup15:00
Lesson 4: Configure Your IDE with Cursor or Visual Studio Code10:00
Lesson 5: API Keys, Environment Variables and .env Files15:00
Lesson 6: Managing Model Costs, Usage Limits and Development Budgets10:00
Lesson 7: Make Your First Model API Call in Python15:00
Lesson 8: Chain Multiple Model Calls into a Workflow15:00
Lesson 9: Handle API Errors, Rate Limits, and Invalid Responses15:00

Lesson 1: What Is an AI Agent?10:00
Lesson 2: AI Agents vs Deterministic Workflows10:00
Lesson 3: Understanding the Agent Loop10:00
Lesson 4: Prompt Chaining and Sequential Workflows10:00
Lesson 5: Routing and Conditional Execution10:00
Lesson 6: Parallelization and Concurrent Model Calls10:00
Lesson 7: Orchestrator-Worker Systems15:00
Lesson 8: Evaluator-Optimizer Loops15:00
Lesson 9: Agentic AI Risks, Failure Modes and Common Traps15:00
Lesson 10: Guardrails, Evaluations and Human Oversight15:00

Lesson 1: Comparing Hosted and Local Language Models10:00
Lesson 2: Configure Multiple Model Providers10:00
Lesson 3: Work with OpenAI-Compatible APIs10:00
Lesson 4: Run Local Models with Ollama15:00
Lesson 5: Select Models by Capability, Cost, Speed and Reliability10:00
Lesson 6: Use a Model as an Evaluator15:00
Lesson 7: The Agent Framework and Tooling Landscape15:00
Lesson 8: How AI Agent Tool Calling Works10:00
Lesson 9: Tool Schemas, Arguments, Results and Validation15:00
Lesson 10: Build a Tool-Calling Agent Loop with Python20:00

Lesson 1: Plan the Professional Digital Twin10:00
Lesson 2: Extract Professional Information from PDF Documents15:00
Lesson 3: Design an Effective Digital Twin System Prompt15:00
Lesson 4: Context Engineering for Personal Knowledge Agents15:00
Lesson 5: Add Document Retrieval and Grounded Responses15:00
Lesson 6: Build the Agent’s Tools15:00
Lesson 7: Handle Multiple Tool Calls15:00
Lesson 8: Add Contact and Notification Capabilities15:00
Lesson 9: Build a Visible Planning and Checklist System10:00
Lesson 10: Create the Gradio Chat Interface15:00
Lesson 11: Refactor the Agent into Reusable Python Modules15:00
Lesson 12: Test the Agent with Real Professional Questions15:00
Lesson 13: Deploy the Professional Digital Twin20:00

Lesson 1: Introduction to the OpenAI Agents SDK10:00
Lesson 2: Async Python and asyncio for Agent Applications15:00
Lesson 3: Agents, Runners, Results and Execution Traces15:00
Lesson 4: Build Your First SDK Agent15:00
Stage 6 — Lesson 5: Stream Agent Responses10:00
Stage 6 — Lesson 6: Add Tools with the Function Tool Decorator15:00
Lesson 7: Validate Tool Inputs and Outputs10:00
Stage 6 — Lesson 8: Add Conversation Memory with Sessions15:00
Stage 6 — Lesson 9: Persist Agent Sessions with SQLite15:00
Stage 6 — Lesson 10: Inspect and Debug Agent Traces COMPLETE15:00

Stage 7 — Lesson 1: Single-Agent vs Multi-Agent Architecture10:00
Stage 7 — Lesson 2: Orchestration by Code vs Orchestration by the Model10:00
Stage 7 — Lesson 3: Run Agents Concurrently with asyncio.gather15:00
Stage 7 — Lesson 4: Use Agents as Tools15:00
Stage 7 — Lesson 5: Build a Manager-Agent Architecture15:00
Stage 7 — Lesson 6: Delegate Work Through Handoffs15:00
Stage 7 — Lesson 7: Pass Context Between Agents15:00
Stage 7 — Lesson 8: Control Agent Responsibilities and Boundaries10:00
Stage 7 — Lesson 9: Handle Multi-Agent Failures15:00
Stage 7 — Lesson 10: Trace a Complete Multi-Agent Run15:00

Stage 8 — Lesson 1: Design the AI Sales Development Architecture10:00
Stage 8 — Lesson 2: Define Prospect and Company Data Models10:00
Stage 8 — Lesson 3: Build the Prospect Research Agent15:00
Stage 8 — Lesson 4: Build the Outreach Strategy Agent15:00
Stage 8 — Lesson 5: Build the Professional Email Writer Agent15:00
Stage 8 — Lesson 6: Build the Sales Quality Reviewer Agent15:00
Stage 8 — Lesson 7: Create Email Drafting and Delivery Tools15:00
Stage 8 — Lesson 8: Coordinate the Sales Agents Through Code15:00
Stage 8 — Lesson 9: Coordinate the Sales Agents with a Manager Agent10:00
Stage 8 — Lesson 10: Add Handoffs Between Sales Specialists15:00
Stage 8 — Lesson 11: Add Structured Outputs with Pydantic15:00
Stage 8 — Lesson 12: Add Human Approval Before Sending15:00
Stage 8 — Lesson 13: Build Sales Quality and Personalization Evaluations15:00
Stage 8 — Lesson 14: Complete the AI Sales Development Team15:00

Stage 9 — Lesson 1: Design the Deep Research System10:00
Stage 9 — Lesson 2: Define the Research Workflow and Agent Responsibilities10:00
Stage 9 — Lesson 3: Build the Research Planning Agent15:00
Stage 9 — Lesson 4: Generate Focused Search Queries15:00
Stage 9 — Lesson 5: Build the Web Search Agent15:00
Stage 9 — Lesson 6: Capture Sources and Evidence15:00
Stage 9 — Lesson 7: Build the Evidence Analysis Agent15:00
Stage 9 — Lesson 8: Build the Research Report Writer15:00
Stage 9 — Lesson 9: Add Structured Research Outputs15:00
Stage 9 — Lesson 10: Coordinate the Research Team Through Code15:00
Stage 9 — Lesson 11: Manage Parallel Research Tasks15:00
Stage 9 — Lesson 12: Assemble a Complete Evidence-Based Report15:00
Stage 9 — Lesson 13: Handle Missing, Conflicting and Insufficient Evidence15:00

Stage 10 — Lesson 1: Refactor the Research System into Python Modules15:00
Stage 10 — Lesson 2: Build the Deep Research Gradio Interface15:00
Stage 10 — Lesson 3: Stream Research Progress to the User15:00
Stage 10 — Lesson 4: Display Plans, Sources and Agent Activity10:00
Stage 10 — Lesson 5: Add Report Export and Email Delivery15:00
Stage 10 — Lesson 6: Create a Research Evaluation Dataset15:00
Stage 10 — Lesson 7: Evaluate Source Quality and Report Completeness15:00
Stage 10 — Lesson 8: Analyze Traces, Latency and Token Usage15:00
Stage 10 — Lesson 9: Add Retries and Failure Recovery15:00
AI Course: Stage 10 — Lesson 10: Deploy the Deep Research Application15:00
Stage 10 — Lesson 11: Complete the OpenAI Agents SDK Module10:00

Requirements

No previous AI or machine-learning experience is required. The course introduces Agentic AI concepts from the ground up.
Basic familiarity with computers, installing software, managing files and using a web browser is recommended.
Basic Python knowledge—such as variables, functions, lists, dictionaries and classes—will be helpful, but the essential concepts are explained throughout the course.
No prior experience with AI agents, large language models, MCP, CrewAI, LangChain, LangGraph, AutoGen or the OpenAI Agents SDK is required.
A Windows, macOS or Linux computer capable of running Python development tools is required. At least 8 GB of RAM is recommended, while 16 GB is preferable for Docker and local-model exercises.
A stable internet connection is required for downloading development tools, accessing model APIs and deploying course projects.
Learners should be comfortable following terminal commands. The course provides step-by-step guidance for the commands used.
You will need permission to install software, including Python, Git, an editor such as Visual Studio Code or Cursor, uv and Docker.
A GitHub account is recommended for accessing course repositories, managing source code and building a project portfolio.
Access to at least one supported AI model API is required for the main projects. Some API usage may involve a small additional cost.
A payment method may be required by third-party model or cloud-service providers. Free tiers and local alternatives are introduced where practical.
A willingness to code, troubleshoot errors and build practical projects is the most important requirement.
Beginners are welcome. You do not need to be an experienced AI engineer or data scientist. If you have basic computer skills and are prepared to follow the guided lessons and complete the hands-on exercises, you can take this course.

Description

Master the technologies and engineering practices behind modern AI agents in this intensive, project-based course.

Across 30 guided stages, you will progress from making your first model API call to designing, building, evaluating and deploying advanced AI agents, multi-agent teams, browser automation systems, coding agents and MCP-powered applications.

You will work with Python, OpenAI Agents SDK, CrewAI, LangChain, LangGraph, AutoGen, Microsoft Agent Framework, Model Context Protocol, FastMCP, Playwright, Docker, Gradio, FastAPI and React. You will also explore frameworks such as Google ADK, Pydantic AI, AWS Strands, Agno and Mastra.

The course begins with the foundations of Agentic AI, including model communication, tool calling, structured outputs, agent loops, context engineering, memory, retrieval, guardrails and human oversight. You will then advance to multi-agent orchestration, browser control, sandboxed code execution, agent-to-agent communication, custom MCP servers, evaluation, observability and deployment.

Learning is centered around eight real-world projects:

Professional Digital Twin Agent — Build an agent that represents your skills, experience and professional portfolio.
AI Sales Development Team — Coordinate specialist agents that research prospects and create professional outreach.
Deep Research Intelligence Team — Build a multi-agent system that plans investigations, gathers evidence and produces comprehensive reports.
Market Intelligence and Stock Screening Crew — Create a CrewAI team that researches companies, applies screening criteria and generates structured analysis.
Sandboxed AI Engineering Team — Deploy four agents that plan, develop, test and document software inside Docker.
Browser Task Sidekick — Build an AI coworker that performs browser research and controlled tasks with human approval.
Agent Application Factory — Create an agent system that generates, tests and launches new AI agent applications.
MCP-Powered AI Trading Floor — Build a multi-agent market research and paper-trading environment powered by custom MCP services.

You will learn how to give agents tools, preserve short-term and long-term memory, retrieve external knowledge, coordinate specialists and execute generated code in controlled environments. You will also build browser-enabled agents, reusable MCP servers, interactive user interfaces and production-ready APIs.

Beyond development, the course covers the engineering practices required to create dependable AI systems: structured validation, guardrails, permissions, human approval, tracing, automated evaluations, failure recovery, cost monitoring and secure deployment.

By the end of the course, you will have a portfolio of eight substantial Agentic AI projects and the practical skills required to design, build, evaluate and deploy intelligent agent applications for real-world business and technical problems.

Who this course is for:

Aspiring AI engineers who want a structured, project-based path into Agentic AI development. Python developers and software engineers who want to build AI agents, multi-agent systems, browser automation tools and MCP-powered applications. Beginners with basic programming knowledge who are ready to learn AI agent development through guided, hands-on projects. Machine-learning engineers and data scientists who want to expand beyond model development into tool-using, autonomous AI applications. Web, backend and full-stack developers interested in integrating AI agents into existing software products and business systems. Automation specialists and no-code developers who want to progress from workflow automation to programmable, intelligent agent systems. Technical founders and entrepreneurs who want to prototype AI-powered products, services and commercial solutions. Freelancers and consultants who want to offer AI agent development, workflow automation and MCP integration services to clients. Technology professionals, solution architects and innovation teams evaluating how Agentic AI can improve research, sales, software development and business operations. Students, graduates and career changers who want to build a practical Agentic AI portfolio featuring eight real-world projects. Developers exploring modern AI frameworks including OpenAI Agents SDK, CrewAI, LangChain, LangGraph, AutoGen, Microsoft Agent Framework and MCP. Anyone who learns best by building and wants to move beyond AI theory and basic chatbots to create complete, deployable agent applications. This course is ideal for learners who are curious about Agentic AI, comfortable working with code and prepared to build practical projects. No previous professional AI or machine-learning experience is required.

Agentic AI Engineer: The Complete AI Agents & MCP Course

What you'll learn

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Course content

Welcome to Agentic AI Engineering6 lectures • 1hr 10min

Stage 2 — Development Environment and First Model Calls9 lectures • 2hr 5min

Stage 3 — Agents, Workflows and Agentic Design Patterns10 lectures • 2hr

Models, Providers and Tool Calling10 lectures • 2hr 10min

Project 1: Professional Digital Twin Agent13 lectures • 3hr 10min

Stage 6 — OpenAI Agents SDK Fundamentals10 lectures • 2hr 15min

Stage 7 — Multi-Agent Orchestration10 lectures • 2hr 15min

Stage 8 — Project 2: AI Sales Development Team14 lectures • 3hr 15min

Project 3: Deep Research Intelligence Team13 lectures • 3hr 5min

Stage 10 — Research Application, Evaluation and Deployment11 lectures • 2hr 35min

Requirements

Description

Who this course is for: