Design and deploy advanced AI agents that are ready for production
AI agents are transforming enterprises by automating workflows, reasoning over data, and solving complex problems together. Many teams still struggle to move from demos to production-ready systems.
This course helps you build, test, and deploy Enterprise AI Agents and multi-agent RAG pipelines using LangChain, CrewAI, and LangGraph. Work in guided Python notebooks, follow weekly milestones, and get live feedback through code review clinics.
🏆 Why It’s Unique:
Learn enterprise-grade design for scale and reliability.
Master context engineering to improve agent reasoning.
Use ready-to-run notebooks for every lesson.
Build multi-agent RAG systems ready for deployment.
Join weekly reviews and guest sessions with industry experts.
- What You’ll Build:
Scalable, memory-sharing agents and modular RAG pipelines with observability and retries.
What you’ll learn
Design, deploy, and scale enterprise-grade AI agents with a hands-on, problem-driven approach for engineers and tech leaders.
Everything you build in this course is grounded in real-world enterprise requirements.
You'll learn how to structure agent workflows for long-term scalability, fault tolerance, and ease of maintenance.
Master the art of context design to ensure your agents perform optimally across tasks.
You’ll learn how to structure prompts, manage conversation state, and layer contextual data so agents can reason, plan, and act.
This course is designed for engineers who want to go beyond demos and learn how to build robust, production-minded RAG Systems.
By the end, you’ll be able to design and deploy scalable multi-agent systems optimized for real-world performance.
Learn directly from Rakesh
Rakesh Gohel
AI leader with 25 years in Agentic AI, Automation, and Intelligent Workflows
Who this course is for
Technical Architects and Data Scientists who want to integrate LLMs into agent workflows using memory, tools, and task planning logic.
Product Managers and AI Solution Strategists translating business needs into AI agent workflows, metrics, and scalable solutions.
Tech Leads and Backend Developers managing multiple agents and services looking to scale, monitor, and orchestrate workflows.
Prerequisites
Python Proficiency
Comfort with writing, debugging, and testing Python code. You will clone repositories, install packages, and build modules from scratch.
Basic Concepts of Language Models
Familiarity with how large language models work and common API patterns (e.g., sending prompts, handling responses).
Software Engineering Principles
Understanding of core software engineering principles such as modularity, version control (Git), unit testing, and debugging.
What's included
Live sessions
Learn directly from Rakesh Gohel in a real-time, interactive format.
Interactive Python Notebooks
Use prebuilt notebooks for easy, step-by-step execution—no complex setup required.
Lifetime access
Go back to course content and recordings whenever you need to.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Private Community + Code Clinics
Daily support, async feedback, and peer collaboration
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
14 live sessions • 33 lessons
Week 1
Jan
15
Week 1 Commencement (Session 1)
Jan
16
Week 1 (Session 2)
Foundations: Concepts of a True Agentic System in Enterprise (Not Just Wrappers)
Week 2
Jan
22
Week 2 (Session 1)
Jan
23
Week 2 (Session 2)
Jan
23
Office Hours (Optional)
Agentic RAG Systems and Multi-Agent Workflows
Schedule
Live sessions
42 hrs / week
Live Classes, Office Hours and More
Thu, Jan 15
3:00 PM—5:00 PM (UTC)
Fri, Jan 16
3:00 PM—5:00 PM (UTC)
Thu, Jan 22
3:00 PM—5:00 PM (UTC)
Projects
14 hrs / week
Dedicated Time given for solving doubts
Async content
42 hrs / week
Learning Material Will be Provided per Session
What makes our course the most holistic Agentic AI program out there?
Frequently asked questions
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$2,399
USD