The Model Context Protocol transforms chaotic multi-agent AI into structured, transparent systems. This live workshop teaches you how to use MCP to orchestrate AI agents with typed context passing, clear tool boundaries, and the observability that makes production orchestration reliable.
By Packt Publishing · Refunds up to 10 days before
Before MCP, orchestrating AI agents meant writing custom glue code for every integration. MCP provides a standard protocol with typed schemas, clear context boundaries, and interoperability across any MCP-compatible system. This workshop teaches MCP orchestration from the foundations through to production patterns.
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.
In this workshop, MCP orchestration means each agent exposes a typed MCP interface defining what inputs it accepts and what outputs it produces. An orchestrator agent dispatches tasks to specialized agents through MCP, passing precisely structured context rather than unformatted text. Responses come back with typed schemas that the orchestrator validates before passing to the next agent. This structured flow is what makes the system debuggable and reliable.
In MCP orchestration, each specialized agent typically acts as an MCP server — it exposes tools and resources that other agents can invoke. The orchestrating agent acts as an MCP client — it discovers available tools from each agent server and invokes them with structured parameters. The workshop covers both sides of the MCP interface and how they work together in a production multi-agent system.
MCP includes structured error handling that the workshop covers in detail. When an agent encounters an error, it returns a typed error response rather than an unstructured failure message. The orchestrator can inspect the error type, apply fallback logic, or escalate to a human reviewer depending on the error severity. This structured error handling prevents silent failures from propagating through the agent system.
Yes. MCP is model-agnostic — an MCP server can use any LLM backend (OpenAI, Anthropic, open source models) as long as it exposes the MCP interface correctly. This is one of MCP's key advantages for agent orchestration: it decouples the orchestration layer from the model layer, letting you mix different providers for different agents based on their specific requirements.
MCP orchestration scales to many agents, but the practical limit is determined by your context management strategy rather than the protocol itself. With the context engineering patterns from this workshop — explicit context boundaries, per-agent semantic blueprints, and the Glass-Box observability layer — you can orchestrate complex multi-agent systems with many specialized agents while maintaining reliability and debuggability.
Yes. MCP has matured significantly in 2026 and is being used in production multi-agent systems. The workshop covers production-ready MCP patterns including error handling, context management, monitoring, and deployment considerations. Denis Rothman uses MCP in production examples throughout 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