AI agents fail in production because they lack proper context engineering. This live 6-hour course teaches you the complete discipline — semantic blueprints, MCP orchestration, memory management, RAG pipelines, and safeguards — so your AI agents actually work reliably.
By Packt Publishing · Refunds up to 10 days before
Developers who understand prompt engineering can make agents work in demos. Developers who understand context engineering can make agents work in production. This course teaches you the architectural discipline that makes the difference between demo AI and production AI.
Context engineering is the discipline of designing AI systems that provide the right information, tools, and context to LLMs at the right time — replacing brittle prompts with reliable, scalable production AI architectures.
Multi-agent systems are AI architectures where specialised agents collaborate to accomplish complex tasks. This workshop shows you how to orchestrate them reliably using the Model Context Protocol and semantic blueprints.
MCP is Anthropic's open standard for connecting AI models to tools, data sources, and other agents. This workshop teaches you to use MCP for building orchestrated multi-agent workflows that are transparent and controllable.
Context engineering and multi-agent systems have almost no quality hands-on resources. This 6-hour live workshop gives you a complete guided build with a bestselling AI author answering your questions throughout.
Six modules. Six hours. A production-ready context engine by the time you finish.
Design structured context that gives AI agents precise, goal-driven contextual awareness beyond simple prompting.
Orchestrate specialised agents using the Model Context Protocol for adaptable, context-rich reasoning workflows.
Engineer retrieval-augmented generation pipelines with citations, memory, and safeguards against hallucination.
Design AI memory systems that maintain context across long conversations and complex multi-step workflows.
Implement moderation, data poisoning protection, prompt injection prevention, and trust mechanisms for production AI.
Build a transparent, traceable Context Engine that gives you complete visibility and control over your AI system.
A working production system — not just architectural knowledge.
A fully working multi-agent system with context engineering
MCP-orchestrated agent workflows you can use in production
High-fidelity RAG pipeline with citations and memory
Semantic blueprints and agent architecture patterns
Production-ready safeguards against hallucination and injection
Certificate of completion from Packt Publishing
Denis Rothman literally wrote the book on context engineering for multi-agent systems — published by Packt in 2025.
Denis Rothman is a bestselling AI author with over 30 years of experience in artificial intelligence, optimisation, and agent systems. He has written multiple cutting-edge AI books for Packt Publishing and is the author of the book “Context Engineering for Multi-Agent Systems.” In this workshop he guides you step by step through the practical architecture of production-ready multi-agent AI systems.
This is an intermediate to advanced workshop. You need the basics below.
Common questions about the workshop, what to expect, and how to prepare.
AI agents fail in production because they receive too much irrelevant context, too little relevant context, or poorly structured context. Context engineering is the discipline of designing what information agents receive, when they receive it, and how it is structured — making the difference between agents that hallucinate and agents that reason reliably.
A prompting course teaches you to write better instructions for a single LLM call. This context engineering course teaches you to design complete AI agent architectures — how agents share information, how context windows are managed across multi-step workflows, how memory is engineered, and how agents are orchestrated through MCP for production reliability.
Yes. The context engineering principles taught in this course — semantic blueprints, context window management, MCP orchestration, RAG architecture, and safeguards — apply to any AI agent framework or project. The instructor covers how to retrofit context engineering principles into existing systems.
The Glass-Box Context Engine is a transparent, traceable multi-agent AI system where you have complete visibility into agent reasoning. Unlike black-box AI systems, it provides audit trails, explainable outputs, and controllable agent behaviour — the properties required for production and enterprise AI deployment.
This is a fully live 6-hour course on April 25, 2026. You interact with Denis Rothman in real time, ask questions about your specific architecture challenges, and build alongside other developers. A recording is shared with all participants after the session.
Intermediate Python experience and basic LLM familiarity are the prerequisites. You should be comfortable with Python classes and APIs. Prior experience building AI agents is helpful but not required — Denis builds from first principles so every participant understands the architecture from the ground up.
6 hours. Live author of the definitive book. Production AI agents by the end. Seats are limited.
Register Now →Saturday April 25 · 9am to 3pm EDT · Online · Packt Publishing