Agentic AI in 2026 is not about demos — it is about building reliable systems that orchestrate agents in production. This live 6-hour workshop gives you the complete architecture: context engineering, MCP, RAG, memory, and safeguards.
By Packt Publishing · Refunds up to 10 days before
Most agentic AI tutorials show you how to chain a few LLM calls. This workshop shows you how to build agentic AI systems that work reliably at scale — using context engineering principles that are just now becoming standard practice for production AI teams in 2026.
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 has 30+ years in AI and wrote the book on context engineering — the foundation of reliable agentic AI.
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.
In 2026, agentic AI means AI systems where specialised agents autonomously handle subtasks, use tools, query data sources, and collaborate to accomplish complex goals — with human oversight for critical decisions. Production agentic AI requires context engineering to manage what each agent knows, memory to persist information across steps, and safeguards to prevent unreliable outputs.
You will build a Glass-Box Context Engine — a production agentic AI system where multiple specialised agents are orchestrated through MCP, each receiving precisely engineered context from semantic blueprints, with high-fidelity RAG for knowledge retrieval and comprehensive safeguards for production reliability.
LLM applications in 2023 were primarily single-turn: prompt in, response out. Agentic AI in 2026 involves multi-step reasoning, tool use, agent collaboration, persistent memory, and autonomous decision-making over extended workflows. Context engineering is the discipline that makes this work reliably.
This workshop uses the Model Context Protocol (MCP) for agent orchestration — the emerging standard for agentic AI integration in 2026. The instructor discusses how the architectural patterns translate to popular frameworks like LangGraph, AutoGen, and CrewAI during the session.
Agentic AI development has a significant learning curve compared to basic LLM usage. This workshop compresses that learning curve dramatically by teaching you the core architectural principles — context engineering, semantic blueprints, MCP — that make everything else make sense. The 6-hour format is intensive but structured for effective learning.
Yes. Production considerations — reliability, safeguards, transparency, monitoring — are built into the architecture from the start rather than treated as an afterthought. The Glass-Box Context Engine you build is production-ready by design.
6 hours. Live bestselling AI author. Production agentic AI system by the end. Seats are limited.
Register Now →Saturday April 25 · 9am to 3pm EDT · Online · Packt Publishing