Orchestrating LLM agents reliably is the hardest problem in AI engineering right now. This live 6-hour workshop teaches you the complete orchestration architecture — semantic blueprints, MCP, context engineering, and production safeguards — for agents that coordinate predictably.
By Packt Publishing · Refunds up to 10 days before
Orchestrating multiple LLM agents means managing context across agents, coordinating tool use, maintaining memory across steps, and handling failure modes gracefully. Without proper context engineering, agent orchestration is unpredictable. This workshop teaches you to make it predictable.
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 systems and wrote the definitive book on context engineering for multi-agent orchestration.
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.
LLM agent orchestration means coordinating multiple AI agents so they collaborate effectively to accomplish complex tasks. It is difficult because each agent operates with incomplete information, agents can misinterpret each other's outputs, context windows fill up across multi-step workflows, and coordinating tool use across agents creates complex dependencies. Context engineering solves these problems architecturally.
You will build a MCP-orchestrated multi-agent system where a coordinator agent manages task decomposition and delegates to specialised agents, each receiving precisely engineered context through semantic blueprints. The architecture includes a shared memory layer, a RAG pipeline for knowledge retrieval, and safeguards that validate inter-agent communications.
MCP provides a standardised protocol for agent-to-tool and agent-to-agent communication, replacing fragile direct API calls with a structured, auditable communication layer. This makes your orchestration architecture more reliable, easier to debug, and compatible with the growing ecosystem of MCP-compatible tools and agents.
Semantic blueprints define the goals, roles, constraints, and context for each agent in your orchestration architecture. They replace ad-hoc prompting with structured context design — ensuring each agent has exactly the information it needs and nothing it does not, preventing context confusion and improving coordination reliability.
Yes. The context engineering and MCP orchestration patterns taught in this workshop apply to any agent framework. Whether you are using LangGraph, AutoGen, CrewAI, or building from scratch, the architectural principles of semantic blueprints, context window management, and MCP integration improve your existing orchestration approach.
Robust error handling and recovery are part of the production safeguards module. The workshop covers how to detect agent failures, how to design fallback behaviours, how to validate agent outputs before they propagate through the system, and how to implement graceful degradation when agents encounter inputs they cannot handle reliably.
6 hours. Live bestselling AI author. Reliable orchestrated agent system by the end. Seats are limited.
Register Now →Saturday April 25 · 9am to 3pm EDT · Online · Packt Publishing