MCP Training - Context Engineering & Multi-Agent AI Systems with MCP

A 2-day intensive course that teaches you how to move beyond basic prompting to build powerful, production-ready Context Engines powered by specialised multi-agent architectures, dynamic RAG, and intelligent orchestration.

Description

 

TTS.

MCP Training — Context Engineering
& Multi-Agent AI Systems

2-Day Intensive Technical Course

Move beyond basic prompting to build powerful, production-ready Context Engines powered by specialised multi-agent architectures, dynamic RAG, and intelligent orchestration.

ttsolutions.com.au  ·  1300 667 577  ·  info@ttsolutions.com.au

A B O U T   T H I S   C O U R S E

Context Engineering & Multi-Agent AI Systems with MCP

This intensive 2-day course equips participants with cutting-edge skills to design, build, and deploy advanced Context Engineering and Multi-Agent AI Systems. Moving far beyond basic prompting and single-model usage, the program teaches how to create sophisticated, reliable, and production-ready AI architectures that leverage structured context, specialised agents, dynamic knowledge retrieval, and intelligent orchestration.

Participants will learn to construct a powerful Context Engine — a unified system that coordinates multiple AI agents, manages complex workflows, and delivers high-quality, goal-aligned outputs at scale.

 

What You Will Leave With

By the end of the 2-day intensive, participants will be able to:

✓Design and build production-ready multi-agent AI systems using MCP
✓Engineer rich, structured context architectures that go far beyond basic prompting
✓Implement dual RAG pipelines with dynamic knowledge retrieval and context management
✓Assemble, harden, and optimise a full Context Engine with orchestration, summarisation, and agent defences
✓Deploy AI systems to production with glass-box observability, moderation guardrails, and policy-driven control
✓Apply the Context Engine to real-world use cases including legal compliance and strategic marketing
 

T W O - D A Y   P R O G R A M

Course Outline

 

Two-Day Program at a Glance

DAY 1  —  Foundations to Multi-Agent Architecture

01 From Prompts to Context — Building the Semantic Blueprint
02 Building a Multi-Agent System with MCP
03 Building the Context-Aware Multi-Agent System
04 Assembling the Context Engine

DAY 2  —  Hardening, Optimisation & Production Deployment

05 Hardening the Context Engine
06 Context Reduction with the Summarizer Agent
07 High-Fidelity RAG and Agent Defences
08 Moderation, Latency & Policy-Driven AI
09 Specialised Application — The Strategic Marketing Engine
10 Blueprint for Production-Ready AI

D A Y   1

Foundations to Multi-Agent Architecture

Semantic context engineering, agent design, RAG pipelines, and the Context Engine

01

From Prompts to Context — Building the Semantic Blueprint

Learn how to move beyond basic prompting by engineering rich, structured context. Participants will explore the five levels of context design and apply Semantic Role Labelling (SRL) to build a semantic blueprint that guides AI toward precise, goal-aligned outputs.

By the end of this module, participants will be able to:

✓Move beyond basic prompting techniques to engineer rich, structured, and reusable context for AI systems
✓Understand and apply the five levels of context design — from basic instructions to fully semantic, layered blueprints
✓Utilise Semantic Role Labelling (SRL) to create precise, goal-aligned semantic blueprints that guide AI behaviour
✓Design context architectures that reduce ambiguity and significantly improve output consistency and quality
✓Practically build and test semantic blueprints for different use cases
Duration: 90 minutes
🔬Lab 1: Build a Semantic Blueprint for a Real-World Use Case
02

Building a Multi-Agent System with MCP

Understand how to architect and implement a Multi-Agent System (MAS) using the Model Context Protocol (MCP). Participants will build and connect specialised agents — Researcher, Writer, and Orchestrator — and learn how to handle errors and validate agent communication.

By the end of this module, participants will be able to:

✓Architect and implement a complete Multi-Agent System (MAS) using the Model Context Protocol (MCP)
✓Design and connect specialised agents including Researcher, Writer, and Orchestrator agents
✓Establish clear communication protocols between agents
✓Implement error handling, validation, and recovery mechanisms for agent-to-agent interactions
✓Debug and optimise inter-agent communication flows
Duration: 90 minutes
🔬Lab 2: Build and Connect a Three-Agent MAS with MCP
03

Building the Context-Aware Multi-Agent System

Extend the MAS by integrating a dual Retrieval-Augmented Generation (RAG) pipeline. Participants will prepare and ingest both procedural and factual knowledge bases, then wire them into a context-aware system where agents retrieve and apply relevant information dynamically.

By the end of this module, participants will be able to:

✓Extend a basic MAS by integrating a dual Retrieval-Augmented Generation (RAG) pipeline
✓Prepare, ingest, and manage both procedural and factual knowledge bases
✓Dynamically wire knowledge sources into agents so they can retrieve and apply relevant information in real time
✓Build systems where agents are context-aware rather than relying solely on static prompts
✓Evaluate and measure the impact of dynamic context retrieval on output relevance and accuracy
Duration: 90 minutes
🔬Lab 3: Integrate Dual RAG Pipelines into Your MAS
04

Assembling the Context Engine

Bring together specialist agents, an Agent Registry, and a central orchestrator into a unified Context Engine. Participants will implement the Planner, Executor, and Execution Tracer components and run the engine end-to-end.

By the end of this module, participants will be able to:

✓Integrate specialist agents, an Agent Registry, and a central Orchestrator into a unified Context Engine
✓Implement core engine components: Planner, Executor, and Execution Tracer
✓Design end-to-end workflows where the engine decomposes complex tasks and coordinates execution
✓Run complete Context Engine cycles from task intake to final output
✓Understand how the Context Engine acts as a higher-order reasoning layer above individual agents
Duration: 90 minutes
🔬Lab 4: Assemble and Run Your First Complete Context Engine

D A Y   2

Hardening, Optimisation & Production Deployment

Reliability, summarisation, RAG defences, moderation, specialised applications, and deployment

05

Hardening the Context Engine

Refactor the Context Engine for real-world reliability. Participants will apply production-level logging, dependency injection, proactive context management, and modular design patterns — then trace and deconstruct the engine’s reasoning step by step.

By the end of this module, participants will be able to:

✓Refactor the Context Engine for real-world reliability and resilience
✓Apply production-level techniques: logging, dependency injection, proactive context management, and modular design patterns
✓Trace and deconstruct the engine’s reasoning step-by-step for debugging and optimisation
✓Identify and mitigate common failure modes in multi-agent systems
✓Build observability into the Context Engine for long-term maintainability
Duration: 75 minutes
🔬Lab 5: Harden and Instrument Your Context Engine
06

Context Reduction with the Summarizer Agent

Implement a Summarizer agent to reduce context size and manage operational costs. Participants will apply micro-context engineering techniques and explore how intelligent summarisation improves both efficiency and output quality.

By the end of this module, participants will be able to:

✓Design and implement a Summarizer Agent to intelligently reduce context size while preserving critical information
✓Apply micro-context engineering techniques to lower token usage and operational costs
✓Balance context compression with output quality through intelligent summarisation strategies
✓Measure and optimise the trade-off between cost, latency, and performance
✓Integrate the Summarizer Agent as a reusable component within larger Context Engines
Duration: 75 minutes
🔬Lab 6: Build and Integrate a Summarizer Agent
07

High-Fidelity RAG and Agent Defences

Build a trustworthy, secure research assistant inspired by NASA-grade reliability standards. Participants will upgrade the RAG ingestion pipeline, implement input sanitisation, and validate the full system for accuracy, safety, and backward compatibility.

By the end of this module, participants will be able to:

✓Build a high-fidelity RAG system meeting NASA-grade reliability standards
✓Upgrade RAG pipelines with advanced input sanitisation, validation, and retrieval quality controls
✓Implement robust agent defences against hallucinations, prompt injection, and other adversarial inputs
✓Ensure backward compatibility while enhancing system trustworthiness and safety
✓Validate the full system for accuracy, security, and production readiness
Duration: 75 minutes
🔬Lab 7: Upgrade RAG and Implement Agent Defences
08

Moderation, Latency & Policy-Driven AI

Architect an enterprise-ready engine with automated moderation guardrails and a policy-driven controller. Participants will apply multi-domain control deck templates and deploy the engine as a legal compliance assistant.

By the end of this module, participants will be able to:

✓Architect enterprise-ready engines with automated moderation guardrails
✓Design and deploy a policy-driven controller using multi-domain control deck templates
✓Optimise for latency while maintaining quality and safety
✓Build AI systems that act as legal compliance assistants by enforcing organisational policies
✓Create configurable policy layers that can be adapted across different industries and use cases
Duration: 75 minutes
🔬Lab 8: Deploy a Policy-Driven Compliance Engine
09

Specialised Application — The Strategic Marketing Engine

Apply the Context Engine to a real-world marketing use case. Participants will design a marketing knowledge base and run competitive analysis, technical-to-marketing copy transformation, and multi-source pitch synthesis use cases.

By the end of this module, participants will be able to:

✓Apply the full Context Engine to a real-world strategic marketing use case
✓Design and populate a marketing knowledge base tailored for competitive intelligence
✓Conduct competitive analysis, technical-to-marketing copy transformation, and multi-source pitch synthesis
✓Build an end-to-end marketing engine capable of generating high-quality, context-aware deliverables
✓Evaluate the business impact of the Context Engine in a specialised domain
Duration: 60 minutes
🔬Lab 9: Build a Strategic Marketing Context Engine
10

Blueprint for Production-Ready AI

Productionise the glass-box engine for enterprise deployment. Participants will cover secrets management, async task queues, containerisation, observability, and learn how to present the business value of a production AI system to stakeholders.

By the end of this module, participants will be able to:

✓Productionise the Context Engine for real deployment environments
✓Implement glass-box design principles for transparency and auditability
✓Manage async task queues, containerisation, and advanced observability
✓Present the business value of a production AI system to technical and non-technical stakeholders
✓Create a complete deployment blueprint covering secrets management, scaling, monitoring, and maintenance
✓Develop a roadmap for evolving the Context Engine post-course
Your Production Blueprint Covers:  Secrets management · Async task queues · Containerisation · Observability · Scaling · Monitoring · Stakeholder presentation
Duration: 90 minutes
🔬Lab 10: Productionise and Deploy Your Context Engine

About our AI training courses…

All of our courses can be attended as online live instructor-led classes, on-demand self-paced, or organised as private training for you and your team.

All of our courses include:

✅Expert instructors with real-world subject matter expertise. We will know the answers to your questions.
✅Hands-on labs in self-contained environments — so you can build and test without risk to production systems.
✅Real-world scenarios, exercises, and codebases designed around actual engineering challenges.
✅Learn-by-doing: every concept is immediately followed by a hands-on lab where you build the real thing.
✅Safe, isolated lab environments with pre-configured tooling — you focus on building, not setup.
✅Access to your expert trainer and our support helpdesk after the course for any questions or issues.
✅Access to your lab environment and all course code after the course is completed.
✅Over the last 20 years, our courses have been attended by thousands of students worldwide. We create courses for Microsoft, Skillsoft, and other learning partners.

Need more than just training?

There are significant risks if AI is not implemented with the right architecture from the start.

These risks can be significantly mitigated by attending one of our courses, raising awareness, and making the right design decisions from the beginning. If you need further help, consider our private engagements where we help you design, build, and deploy your AI architecture.

The risks:

✅Brittle single-agent architectures that fail unpredictably in production
✅Context window mismanagement leading to degraded output quality and excessive costs
✅No RAG pipeline resulting in hallucinations and unreliable knowledge retrieval
✅Lack of agent defences leaving systems vulnerable to prompt injection and adversarial inputs
✅Poor observability making it impossible to debug or audit multi-agent behaviour
✅No moderation or policy controls creating compliance and governance exposure
✅Improper productionisation resulting in systems that work in demos but fail at scale

How we can help:

✅Context Engine architecture review and design for your specific use case
✅Hands-on build engagement — we work alongside your team to build your first production Context Engine
✅RAG pipeline design, implementation, and optimisation for your knowledge bases
✅Agent defence and moderation framework implementation
✅Production deployment support: containerisation, observability, scaling, and monitoring

Contact us for more options and to book a free one-on-one consultation.

Need more than training? We can design a tailored delivery for your engineering team and run it onsite or online.

Use our Contact Form to arrange a complimentary 1-hour discovery workshop to discuss your requirements.

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