Artificial Intelligence , Computer Vision Engineering
AI Agent Engineering: Autonomous Multi-Agent Systems, Workflow Orchestration & Intelligent Automation
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Teacher

Ragul Muthukumar

Last Update:

January 19, 2022

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Master the engineering of autonomous AI agents by designing intelligent multi-agent systems capable of reasoning, planning, memory management, tool invocation, workflow orchestration, and enterprise automation using LangGraph, LangChain, CrewAI, AutoGen, DSPy, MCP, and production-grade AI infrastructure.

AI Agent Engineering: Autonomous Multi-Agent Systems, Workflow Orchestration & Intelligent Automation
User Avatar

AI Agent Engineering: Autonomous Multi-Agent Systems, Workflow Orchestration & Intelligent Automation

10 Weeks
All levels
11 lessons
0 quizzes
0 students

Agentic Artificial Intelligence is transforming enterprise software by enabling autonomous systems to reason, plan, collaborate, and execute complex workflows with minimal human intervention. This course provides a comprehensive guide to designing production-grade AI agents capable of solving real-world business problems using modern Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, memory architectures, and workflow orchestration frameworks.

Students will build AI copilots, autonomous assistants, research agents, software engineering agents, customer support agents, and enterprise automation systems using LangChain, LangGraph, CrewAI, AutoGen, DSPy, MCP (Model Context Protocol), FastAPI, Docker, Kubernetes, Redis, PostgreSQL, and cloud-native deployment platforms.

The curriculum emphasizes reasoning strategies, tool calling, function execution, multi-agent collaboration, long-term memory, vector retrieval, observability, AI governance, and scalable production deployment.

A working knowledge of Python and basic machine learning is recommended. The course introduces LLM concepts before advancing into agent engineering.
You'll work with LangChain, LangGraph, CrewAI, AutoGen, DSPy, and the Model Context Protocol (MCP), along with vector databases and enterprise deployment tools.
Yes. You'll develop production-ready copilots, research assistants, customer support agents, DevOps automation agents, and enterprise workflow orchestrators.
Yes. You'll implement Retrieval-Augmented Generation using FAISS, Pinecone, and Qdrant to provide agents with accurate, context-aware responses.

Features

  • LangChain & LangGraph Development
  • MCP Integration Examples
  • Architecture Diagrams & Templates
  • Certificate of Completion
  • Multi-Agent System Design Workshops

Target audiences

  • LLM Engineers
  • Machine Learning Engineers
  • Backend Developers
  • Enterprise Solution Architects
  • Graduate Students in AI & Computer Science

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AI Agent Engineering: Autonomous Multi-Agent Systems, Workflow Orchestration & Intelligent Automation
$29.00
- 68% OFF
  • Instructor : Ragul Muthukumar
  • Lectures : 11
  • Duration : 10 weeks
  • Enrolled : 0 students
  • Language: :English

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