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AI Architecture Clarity: Understanding LLM, RAG, Agents, and MCP Through the Brain Model

A clear executive guide explaining LLM, RAG, AI Agents, and MCP using the Brain model to prevent enterprise AI instability.

...CIOs, CTOs, Enterprise Architects, AI Transformation Leaders Problem it solves: Confusion around LLM, RAG, Agents, and MCP leading to unstable AI initiatives Key outcome: Clear architectural layering, reduced...

Technology Strategy Execution Ai Governance Ai Ai Strategy Read Insight

AI Knowledge Systems — RAG, Search, Memory & Internal Knowledge

Intermediate4-5 hoursKnowledge system map + RAG decision framework + memory design checklist

Many useful AI systems are not smarter because the model is smarter. They are smarter because the system can retrieve, search, remember, and ground itself in the right knowledge at the right time.

...smarter than it is Why model knowledge is not enough Search, retrieval, memory, and grounding When to use RAG Internal knowledge design Chunking, freshness, and authority Failure patterns in knowledge systems Design...

1 DaySystems Thinking

Knowledge DesignPractical

RAG Search Memory Knowledge Systems Internal Knowledge View Details

AI Implementation & Technical Architecture

Intermediate to Advanced5-7 hoursAI architecture blueprint + MLOps framework + deployment playbook

Turning AI prototypes into reliable, scalable production systems. MLOps, model serving, RAG architecture, monitoring, and the engineering discipline that most AI projects are missing.

...Navigation The implementation gap MLOps — the operational backbone Model serving — making models accessible RAG architecture — grounding AI in your data Infrastructure design Monitoring — watching for silent degradation Scaling The...

2 DaysIntensive

ArchitectureBlueprint

AI Architecture MLOps Model Deployment AI Infrastructure Production AI View Details

Data and AI

Intermediate4-6 hoursData readiness assessment + governance framework + pipeline blueprint

The data foundations that make AI work. Data quality, governance, architecture, pipelines, and the GenAI shift from training to retrieval. Most AI failures are data failures — learn the patterns and prevent them.

...actually learn from. Technique What it does Example Aggregation Summarises data over time or groups Average spend per customer per month Transformation Converts data into useful formats Date → day of week,...

1.5 DaysIntensive

Data ReadinessAssessment

Data Quality Data Governance Data Architecture Feature Engineering Data Pipelines RAG Vector Databases GenAI Data AI Fluency View Details

How LLMs Work

Beginner to Intermediate4-6 hoursWorking mental model + reference materials

What actually happens inside a Large Language Model — from the moment you type a prompt to the moment it responds. No math. No fluff. Just a clear picture you can hold in your head.

...say Step 6 — Sampling: how it chooses a word Step 7 — The Loop: how it builds a full response Extension — RAG: how it looks things up Training: how it learned all this Hallucination: why it sometimes lies confidently The...

2 DaysIntensive

Beginner FriendlyNo math required

LLMs Machine Learning Transformers Generative AI View Details

AI Evaluation, Testing & Validation

Intermediate to Advanced4-6 hoursEvaluation framework + test matrix + validation workflow

If you cannot evaluate an AI system, you cannot trust it. Learn how to test prompts, retrieval, agents, workflows, and production behaviour before confidence turns into risk.

...you evaluating Offline tests, live tests, and human review Metrics that matter Evaluating prompts, RAG, and agents Test set design and edge cases Validation as an operating habit What "good enough" actually...

1 DayHands-on

ValidationCore Discipline

AI Evaluation Testing Validation Quality Assurance Production AI View Details

Data & AI Direction

Intermediate to Advanced (SA, EA, TS)5-6 hoursData and AI direction framework + AI readiness assessment + governance overlay

How data strategy and AI direction connect to architecture — covering data quality, governance, data architecture for AI, AI enablement, responsible AI, data products, data mesh, and building a data and AI roadmap.

...The AI data platform architecture has layers: Ingestion — batch + streaming + document ingestion. Storage — data lake + warehouse + vector store. Processing — ETL/ELT + feature engineering + embedding generation....

1.5 DaysIntensive

CurrentSA · EA · TS

Architecture Data Strategy AI Data Governance Data Mesh Data Products View Details

Automation & Agentic Systems

Intermediate to Advanced (SA, EA, TS)6-7 hoursAutomation opportunity map + agent architecture blueprint + MCP/A2A integration guide + Agentic Risk & Cost Framework

How AI-powered architectures that plan, act, and use tools autonomously are reshaping integration and workflow — covering agentic patterns, MCP and A2A protocols, orchestration frameworks, human-in-the-loop design, guardrails, observability, and economic risk management.

...What it does Architecture implication Planning Breaks a goal into steps Planner component; plan storage Tool use Invokes external tools (APIs, databases, code) Tool registry; tool calling protocol Memory...

1.5 DaysIntensive

CurrentSA · EA · TS

Architecture AI Agents Agentic Systems MCP A2A Automation Orchestration View Details

AI Agents and Automation

Intermediate to Advanced6-8 hoursAgent use case map + architecture blueprint + governance framework

AI agents — what they are, how they work, the protocols shaping them (MCP, A2A), and the governance you need before you let one act on your behalf. The bridge between AI that talks and AI that does.

...Type Scope Duration Example Short-term Current task Session "I've already gathered 3 of 5 data points" RAG External knowledge On-demand "Retrieve relevant policy documents" Long-term Cross-session Persistent...

2 DaysIntensive

Agent DesignWorkshop

AI Agents Agentic AI MCP A2A Multi Agent Systems AI Orchestration View Details

AI Capabilities, Limitations & Use Cases

Beginner to Intermediate3-4 hoursTask-to-AI fit judgment + reference materials

What AI is genuinely good at, where it will let you down, and how to match the right task to the right tool. The judgment you need before you touch a prompt.

...into a 3-bullet summary e.g. Rewrite for a non-technical audience e.g. Convert to a formal proposal paragraph Which transformation worked best? Which needed the most editing? This tells you where AI saves...

Half DayIntensive

Beginner FriendlyNo math required

Generative AI AI Capabilities AI Limitations Hallucination Use Cases AI Fluency View Details

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