Note: This course is an independent offering and is not affiliated with, endorsed by, or related to the instructor's past employers.

AI agents are powering intelligent applications, automating tasks, reasoning over data, and working together to solve complex problems.

Many builders find it hard to go beyond simple demos and build systems that are ready for production. You may have used frameworks like LangChain or tools such as CrewAI, but chaining multiple agents, managing shared memory, orchestrating tools, and meeting performance targets in a live environment can still feel overwhelming.

This course uses a problem-driven approach to show you how to build, test, and deploy multi-agent Retrieval-Augmented Generation (RAG) pipelines. You will work with code at every step. At each milestone, you will see clear progress metrics, complete hands-on assignments, and receive real-time feedback. After four weeks, you will have a fully functional multi-agent application with monitoring and benchmarking data.

🏆 Why This Course Stands Out:

1. Enterprise-Ready Design Principles

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.

2. Structured Timeline and Milestones

We know poor pacing and unclear timelines can derail your learning. Every week has clearly defined start and stop dates, deliverables, and checkpoints. At the start of each module you will see exactly which assignments are due and what you should be able to demonstrate by the end. Progress tracking is built into the portal so you never lose sight of deadlines or miss a key deliverable.

3. Build and Deploy Multi-Agent RAG Systems

This course is designed for engineers who want to go beyond demos and learn how to build robust, production-minded Retrieval-Augmented Generation (RAG) systems using multiple AI agents. By the end, you’ll be able to design and deploy scalable multi-agent systems optimized for real-world performance.

4. Hands-On, Production-Focused Assignments

Theory alone is not enough. Every lesson has a coding exercise with practical requirements, like reducing latency, cutting token costs, dealing with tool errors, and passing data between agents. You will not just copy paste example code; you will adapt and extend it to fit your own use cases. By week four you will deploy a multi-agent system that ingests data, runs planning flows, and outputs final results to a live endpoint.

5. Weekly Live Code Review Clinics

Every week, you'll participate in office hours dedicated to reviewing your agent designs and implementation strategies. Instead of generic Q&A sessions, you will bring your code and current blockers to weekly live clinics. The instructor and community will review your design choices, spot logic errors, and suggest concrete improvements to your architecture. These sessions keep you accountable and help you iterate quickly.

6. Simplified Toolchain and Environment Setup

Complex dependencies and GPU requirements can be a barrier. All tools, libraries, and environments are preconfigured in Docker containers. You will work in a virtual lab that mirrors production as closely as possible, without needing to provision GPUs or debug conflicting package versions.

7. Guest Lectures from Industry Practitioners

Each week you will hear from engineers and architects who have built AI agent services at scale. They will walk you through real deployment stories, show you the metrics they tracked, and share how they overcame challenges in multi-agent coordination, Error handling, and cost optimization.

💻 What You’ll Build:

1. Design scalable agent systems that coordinate tasks, share memory, and operate in real-time environments.

2. Implement multi-agent RAG pipelines with persistent memory, tool orchestration, and modular planning flows.

3. Build an enterprise-ready agentic system from scratch, applying production-grade design patterns including observability, retry mechanisms, and multi-agent orchestration that can scale in business-critical environments.

4. Deploy your system using real-world metrics.

⚙️ Tools You’ll Use



1. LangChain and LangGraph for defining agent logic, managing memory state, and orchestrating complex interactions with tools via ReAct and state machines.

2. CrewAI for coordinating multi-agent setups through role assignment, task planning, and collaborative workflows.

3. Agent Development Kit (ADK) for mapping agents to business-grade use cases using composable modules and evaluation-driven design.

✅ Who This Course Is For:

1. Technical Architects and Data Scientists

You already understand data pipelines and model basics. You want to learn how to integrate language models into agent workflows and design systems that use tools, maintain memory, and plan across tasks.

2. Engineers and Full-Stack Developers

You have Python skills and a background in software engineering. You are now looking to transition into AI roles by building production-grade LLM agent applications.

3. Tech Leads and Backend Developers

You need to coordinate multiple services and agents in your organization. You want to learn best practices for orchestration, monitoring, and scaling multi-agent workflows in real business scenarios.

4. Technical Consultants and Strategists

You advise clients on AI roadmaps or make decisions about AI investments. You need a deep understanding of how agentic systems work in practice, beyond high-level strategy or prompting tips.

❌ Who This Course Is Not For:

1. Absolute Beginners in Coding

If you are new to Python or general software development, start with a solid programming course first. This course moves quickly and assumes familiarity with writing, debugging, and testing code.

2. You are a Researcher in Model Building and Theory

This is a hands-on, practical course that uses existing language models rather than teaching transformer internals, fine-tuning, or building models from scratch.

3. No-Code Enthusiasts

This course will work directly with code, APIs, and orchestration libraries rather than using visual, low-code platforms. If you want to build applications without writing code, you may find this course more technical than expected.