This live context engineering tutorial does not just explain the concepts — it walks you through building a complete Glass-Box Context Engine in 6 hours. Semantic blueprints, MCP orchestration, high-fidelity RAG, and production safeguards, all implemented in real Python code.
By Packt Publishing · Refunds up to 10 days before
Most context engineering tutorials cover individual concepts in isolation. This live tutorial integrates everything — semantic blueprints, MCP, RAG, Glass-Box architecture, and safeguards — into a single coherent system build. You leave with working code and a deep understanding of how all the pieces connect.
Context engineering is the discipline of designing systems that give AI the right information, in the right format, to reason and act reliably. It goes beyond prompt engineering — building structured, deterministic systems that scale in production rather than depending on fragile prompts.
A multi-agent system uses multiple specialized AI agents working together — each with a defined role, context, and tools — to complete complex tasks no single agent could handle reliably. Context engineering is the key to making them work predictably.
MCP is Anthropic's open standard for connecting AI models to tools, data sources, and other agents. It provides a structured way to orchestrate multi-agent workflows with clear context boundaries — making systems transparent and debuggable.
Context engineering concepts require hands-on practice to truly understand. This live workshop lets you build a working system with a world-class instructor answering your questions in real time — far more effective than reading documentation alone.
Six modules. Six hours. A production-ready context-engineered AI system by the time you finish.
Understand why prompts fail at scale and how semantic blueprints give AI systems structured, goal-driven contextual awareness that scales reliably.
Design and orchestrate multi-agent workflows using the Model Context Protocol. Build transparent, traceable agent systems that coordinate reliably.
Build retrieval augmented generation pipelines that deliver accurate, cited responses. Engineer memory systems that persist context reliably across agent interactions.
Architect a transparent, explainable context engine where every decision is traceable. Build AI systems that are predictable and debuggable in production.
Implement safeguards against prompt injection and data poisoning. Enforce moderation, trust boundaries, and access controls in multi-agent environments.
Deploy your context-engineered multi-agent system to production. Apply patterns for scaling, monitoring, and maintaining reliability under real-world load.
Concrete working deliverables — not just theory and slides.
A working Glass-Box Context Engine with transparent, traceable reasoning
Multi-agent workflow orchestrated with the Model Context Protocol
High-fidelity RAG pipeline with memory and citations
Safeguards against prompt injection and data poisoning
Reusable architecture patterns for production AI systems
Certificate of completion from Packt Publishing
Denis Rothman brings decades of production AI engineering experience to this live workshop — making complex context engineering concepts immediately actionable.
Denis Rothman is a bestselling AI author with over 30 years of experience in artificial intelligence, agent systems, and optimization. He has authored multiple cutting-edge AI books published by Packt and is renowned for making complex AI architecture concepts practical and immediately applicable. In this workshop he guides you step by step through building production-ready context-engineered multi-agent systems — answering your questions live throughout the 6-hour session.
This is an intermediate to advanced workshop. Solid Python and basic LLM experience required.
Common questions about the workshop, what to expect, and how to prepare.
This tutorial builds a complete Glass-Box Context Engine — a production-ready multi-agent AI system with semantic blueprint orchestration, MCP-coordinated agent communication, high-fidelity RAG with citation tracking, transparent observability across all decisions, and safeguards against the most common production failure modes. Every component is implemented in Python during the live 6-hour session.
A live context engineering tutorial gives you real-time feedback when you get stuck, an instructor who adapts explanations to questions that arise, the ability to see working code rather than pseudocode, and the motivation of building alongside others. Written documentation tells you what to do. This tutorial shows you how to do it and why the architecture decisions are made the way they are.
You need intermediate Python experience, some familiarity with LLMs and AI APIs, and ideally some experience with multi-agent systems or agent frameworks. The tutorial is designed for developers who understand the basics but want to build production-grade context engineering skills. Complete beginners to LLMs would find the tutorial moves too fast.
Yes, but efficiently. Each concept is introduced with just enough theory to understand why it is needed before being implemented in code. The ratio is approximately 20% theory to 80% hands-on implementation. Denis Rothman's 30+ years of AI experience means he can explain the theoretical foundations clearly without excessive academic detailing.
This tutorial goes beyond the books by providing live, interactive implementation with a working system as the deliverable. The books cover the concepts comprehensively. This tutorial takes those concepts into a 6-hour hands-on build session where you implement them directly. Many participants read the books first and attend this tutorial to solidify their understanding through practice.
The Glass-Box Context Engine architecture and context engineering principles taught in this tutorial are designed for durability. While specific library versions and MCP implementations evolve, the architectural thinking — semantic blueprints, explicit context management, transparency-first design — remains foundational. The recording you receive reflects the 2026 state of the art at the time of the session.
6 hours. Bestselling AI author. Production context-engineered multi-agent system by the end. Seats are limited.
Register Now →Saturday April 25 · 9am to 3pm EDT · Online · Packt Publishing · Cohort 2