Knowing what MCP is and knowing how to use it effectively for multi-agent AI systems are different things. This live workshop teaches the practical patterns: how to design agent interfaces, how to structure agent orchestration, and how to make MCP-coordinated multi-agent systems reliable in production.
By Packt Publishing · Refunds up to 10 days before
Most developers understand MCP concepts quickly. The challenge is knowing how to apply them: how many MCP servers to create, how to design tool schemas for agent communication, how to handle failures gracefully, and how to make the overall multi-agent system debuggable. This workshop covers the practical application layer.
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.
A multi-agent system uses multiple specialised AI agents working together — each with a defined role, context, and tools — to complete complex tasks no single agent could handle reliably. Context engineering makes them predictable.
MCP is Anthropic's open standard for connecting AI models to tools, data sources, and other agents. It provides structured agent orchestration with clear context boundaries — making systems transparent and debuggable.
Context engineering requires 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.
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 structured, goal-driven contextual awareness.
Design and orchestrate multi-agent workflows using the Model Context Protocol. Build transparent, traceable agent systems.
Build RAG pipelines that deliver accurate, cited responses. Engineer memory systems that persist context reliably across agents.
Architect a transparent, explainable context engine where every decision is traceable and debuggable in production.
Implement safeguards against prompt injection and data poisoning. Enforce trust boundaries in multi-agent environments.
Deploy your context-engineered system to production. Apply patterns for scaling, monitoring, and reliability.
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.
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. He guides you step by step through building production-ready context-engineered multi-agent systems — answering your questions live throughout the 6-hour session.
Intermediate to advanced workshop. Solid Python and basic LLM experience required.
Everything you need to know before registering.
The right number of MCP servers corresponds to the number of distinct specialised capabilities in your system, not the number of agents. Each MCP server should expose a coherent set of related tools that belong together: a retrieval server for all RAG operations, a synthesis server for document generation, a validation server for output checking. Avoid creating one MCP server per tool or one per agent interaction, as this creates unnecessary network overhead.
Effective MCP tool schemas for agent communication have: descriptive names that clearly indicate the tool's purpose, detailed descriptions that tell the LLM orchestrator exactly when to invoke the tool, typed parameters with validation constraints that prevent invalid invocations, and structured return types that downstream agents can parse without ambiguity. The workshop covers schema design patterns that make tools self-documenting and robust.
The recommended orchestration pattern uses a central orchestrating agent as an MCP client that manages the workflow, and specialised agents as MCP servers that handle specific capabilities. The orchestrator dispatches tasks by invoking MCP tools on specialised servers, collects typed results, and synthesises the final output. This hub-and-spoke pattern is simpler to debug and monitor than peer-to-peer agent communication.
Context pollution prevention with MCP uses two techniques: tool schema design that includes only the specific context fields each agent needs (rather than passing the full conversation state), and resource definitions that provide shared context as structured, typed objects rather than raw text. Agents request only the specific context they need through typed parameters, preventing the accumulation of irrelevant context that causes performance degradation.
Monitoring MCP-orchestrated systems uses the Glass-Box logging layer to capture every tool invocation: the calling agent, the target server, the tool name, input parameters, response time, and result type. This telemetry gives you full visibility into agent coordination patterns, identifies slow tool invocations that are bottlenecking the orchestration, and tracks error rates per tool that indicate reliability issues in specific agent capabilities.
Yes. MCP is a network protocol designed for distributed deployment. Each MCP server can run in its own container or on its own machine, communicating with the orchestrating agent client over the network. The workshop covers the network configuration for distributed MCP deployments including service discovery patterns that let the orchestrator find available agent servers without hardcoding their addresses.
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