Learn Agentic AI – Build Multi-Agent Automation Workflows

Learn Agentic AI – Build Multi-Agent Automation Workflows

Learn Agentic AI – Build Multi-Agent Automation Workflows is a practical, hands-on course focused on building autonomous, multi-agent AI systems using Microsoft AutoGen and the Model Context Protocol (MCP). The course is designed to help learners move beyond single-agent chatbots and into collaborative AI agents that can reason, coordinate, self-correct, and execute complex workflows.

Rather than treating Agentic AI as theory, the course emphasizes real-world automation scenarios, especially in software testing, QA, and enterprise workflows. Learners build multiple specialized agents that work together as a system, reflecting how agentic AI is being adopted in modern organizations.

Course Snapshot

• Instructor: Rajul Shetty

• Students Enrolled: 14,009

• Rating: 4.6 / 5

• Content Length: ~10 hours

• Skill Level: Beginner to Intermediate

• Language: English (Auto captions, French Auto)

• Certification: Certificate of completion included

• Access: Full lifetime access (mobile & TV supported)

What This Course Actually Covers

This course introduces the core foundations of Agentic AI and gradually progresses toward building end-to-end multi-agent systems. Learners start with Large Language Models (LLMs) and AI agent fundamentals, then move into AutoGen-based architectures where multiple agents collaborate to solve real problems.

A strong emphasis is placed on automation workflows, context engineering, and agent coordination—key skills needed to build reliable, production-ready agentic systems.

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Skills & Concepts You’ll Work With

Agentic AI & Multi-Agent Systems

• Understanding LLMs, AI agents, and agentic architectures

• Designing systems where multiple agents collaborate toward a goal

• Managing agent coordination, termination strategies, and validation

AutoGen Framework

• Building multi-agent workflows using Microsoft AutoGen

• Working with assistant agents and human-in-the-loop collaboration

• Designing agent conversations and group-chat workflows

Model Context Protocol (MCP)

• Understanding MCP as the backbone for agent-tool communication

• Configuring MCPs for real-world automation scenarios

• Managing context flow across multiple agents

Specialized AI Agents

• Jira Agent for bug analysis and issue tracking

• Playwright Agent for browser and UI automation

• API Agent for service and integration testing

• Database Agent for data analysis and validation

Context Engineering & Agent Design

• Engineering prompts and context for goal-driven agents

• Enabling agents to self-correct and validate outputs

• Applying the Agent Factory Pattern to build reusable agent components

Who This Course Is Best Suited For

• AI Engineers and AI Automation Practitioners

• QA Engineers and Software Testers

• Software Developers and Software Engineers

• Business Analysts and Technical Managers

• Students exploring Agentic AI and multi-agent systems

Common Questions Learners Ask

Do I need prior experience with AutoGen or Agentic AI?
No. The course starts from the basics and builds concepts step by step.

Is Python required?
Basic Python knowledge helps, but Python fundamentals are covered in the final section as optional learning.

Is this course more theoretical or practical?
It is strongly practical, with hands-on projects and real automation agents.

Does it cover real-world use cases?
Yes. Agents are built for browser automation, APIs, databases, and issue tracking systems like Jira.

Practical Value

The main value of this course lies in its system-level approach to Agentic AI. Instead of focusing on isolated agents, learners build multi-agent ecosystems that reflect how AI agents are used in enterprise automation, testing pipelines, and intelligent decision-making systems.

The use of AutoGen, MCP, and reusable agent patterns makes the skills learned directly applicable to real production environments.

Final Thoughts

If you want to move from experimenting with AI tools to engineering collaborative, autonomous AI systems, this course provides a clear and practical path. It’s especially well suited for learners interested in AI-driven automation, software testing, and multi-agent architectures.