Rakesh Gohel

Build, collaborate, and demo advanced multi-agent AI systems together.

Cohort 1

Understanding and developing an Enterprise Mindset for Building Agentic AI that scales.

Cohort 2

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Design, deploy, and scale enterprise-grade AI agents with a hands-on, problem-driven approach for engineers and tech leaders.

Founder @ JUTEQ Inc.

AI leader with 25 years in Agentic AI, Automation, and Intelligent Workflows

AI Agent Engineering: From ReAct, Agentic RAG to Multi-Agent Orchestration

Cloud Solution Architect

Sr Advisor

Technical Leader,Engineering

Data Scientist

Postdoc researcher

Cofounder

Director of AI 

Principal Architect

Data Engineer

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:<ul><li>Learn enterprise-grade design for scale and reliability.</li><li>Master context engineering to improve agent reasoning.</li><li>Use ready-to-run notebooks for every lesson.</li><li>Build multi-agent RAG systems ready for deployment.</li><li>Join weekly reviews and guest sessions with industry experts.</li></ul>- What You’ll Build: Scalable, memory-sharing agents and modular RAG pipelines with observability and retries.

Design and deploy advanced AI agents that are ready for production

Enterprise-Ready Design Principles

Context Engineering for AI Agents

Build and Deploy Multi-Agent RAG Systems

Comfort with writing, debugging, and testing Python code. You will clone repositories, install packages, and build modules from scratch.

Python Proficiency

Familiarity with how large language models work and common API patterns (e.g., sending prompts, handling responses).

Basic Concepts of Language Models

Understanding of core software engineering principles such as modularity, version control (Git), unit testing, and debugging.

Software Engineering Principles

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.

Learn directly from Rakesh Gohel

Rakesh Gohel

Courses

AI Agent Engineering: From ReAct, Agentic RAG to Multi-Agent Orchestration