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Build Your Own Autonomous AI Agent

The Agent Architecture Blueprint™ — From Framework Dependency to Autonomous Systems Mastery

The ONLY masterclass teaching autonomous agent architecture from scratch—build the ReAct loops, tool orchestrators, memory systems, and multi-agent coordination that frameworks hide from you.

This is not another course on using agent frameworks. This is executive business education (Harvard/MIT/Stanford caliber) merged with a masterclass for tech founders and autonomous systems architects. Using the DrLee.AI Shu-Ha-Ri learning method, you'll go from framework-dependent to autonomous systems architect in 9 transformative modules.

Each module begins with a TedTalk-style presentation on architecture strategy, then you immediately build it yourself with hands-on coding. You'll implement ReAct loops, design MCP-compliant tool orchestration, build memory systems, deploy multi-agent coordination, and create the infrastructure that powers autonomous execution—not just API calls.

Different from using LangChain/AutoGPT: While frameworks abstract away the complexity, this course teaches you to build the infrastructure yourself—own the ReAct loops, tool orchestrators, memory modules, and multi-agent protocols. When your agent fails at 2am, you'll know exactly why and how to fix it. Framework users are commoditized. Infrastructure builders are irreplaceable.

By the end, you won't just understand how agents work—you'll own production-ready autonomous agent systems with multi-agent coordination that become your competitive moat.

FROM
Framework User
$120K-$180K · Black-Box Agents
TO
Agent Systems Architect
$200K-$400K · Infrastructure Owner
9 modules · 45 hours · Build production agent systems with 95%+ reliability
Start Your TransformationSee The Journey
The Agent Architecture Blueprint™

Your 9-Step Transformation Journey

Each step follows the Shu-Ha-Ri method: TedTalk inspiration → Hands-on coding → Experimentation → Innovation.Watch as you progress from framework-dependent to autonomous systems architect, building your infrastructure ownership moat with every step.

Weeks 1-3

PHASE 1: Foundation

Specialization & Architecture Mastery

FROM
“Using pre-built frameworks as black boxes, no understanding of agent internals, framework-dependent implementations”
TO
“Architecting custom agent systems from first principles, understanding every component, owning the infrastructure”
🛡️ Agent Architecture Expertise
Ability to design and build autonomous systems without framework dependency—understanding ReAct loops, tool orchestration, and memory systems at the architectural level
Weeks 4-6

PHASE 2: Advanced Capabilities

Knowledge, Memory & Planning Systems

FROM
“Stateless agents with no learning, generic knowledge, single-shot execution”
TO
“Agents with specialized knowledge, persistent memory, multi-step planning capabilities”
🛡️ Advanced Agent Capabilities
Building agents that learn from experience, leverage domain-specific knowledge, and plan complex multi-step executions
Weeks 7-9

PHASE 3: Multi-Agent & Production

Coordination, Code Generation & Scale

FROM
“Single-agent systems, manual code updates, local deployments only”
TO
“Multi-agent coordination, self-improving code agents, production-scale deployments”
🛡️ Production Agent Systems
Enterprise-ready multi-agent architectures with self-improvement capabilities and production deployment expertise

The Complete Transformation Matrix

Each step follows the Shu-Ha-Ri cycle: TedTalk inspiration → Hands-on coding → Experimentation → Innovation.This is the guided progression that transforms framework-dependent developers into autonomous systems architects who own their agent infrastructure.

1

Module 1: Agent Fundamentals & LLM Architecture

FROM (Point A)
“Confusion about what 'AI agent' means, treating agents as chatbots, no framework for agent thinking”
TO (Point B)
“Clear agent architecture understanding, knowing when agents add value over LLMs, designing agent systems strategically, understanding LLM role as agent 'brain'”
🛡️ Strategic agent architecture knowledge—ability to distinguish when autonomous systems are needed vs simple LLM calls
2

Module 2: Tool Integration & Orchestration

FROM (Point A)
“Agents that only output text, no ability to take actions, manual execution of agent suggestions, fragmented tool integrations”
TO (Point B)
“Agents that use tools autonomously, unified tool orchestration layer, automatic execution with safety, MCP-compliant tool architecture”
🛡️ Tool orchestration expertise—ability to design unified tool interfaces that scale to dozens of tools without custom integration per tool
3

Module 3: ReAct Loop Implementation

FROM (Point A)
“One-shot LLM responses, no iterative reasoning, can't recover from errors, shallow problem-solving”
TO (Point B)
“ReAct loop (Thought → Action → Observation), multi-step autonomous execution, self-correction capability, deep problem-solving”
🛡️ ReAct architecture mastery—ability to implement the core autonomous agent loop that enables iterative reasoning and self-correction
4

Module 4: Knowledge Augmentation with RAG

FROM (Point A)
“Agents limited to LLM training data, no domain expertise, generic responses, can't use proprietary knowledge”
TO (Point B)
“Agents with specialized knowledge bases, domain-specific expertise, context-aware responses, leveraging proprietary data”
🛡️ Agentic RAG expertise—building knowledge systems that agents can actively query for domain-specific decision-making
5

Module 5: Memory & Continuous Learning

FROM (Point A)
“Stateless agents (no memory between executions), repeating mistakes, no personalization, can't learn from interactions”
TO (Point B)
“Agents with persistent memory, learning from experience, personalized behaviors, continuous improvement”
🛡️ Memory architecture mastery—building agents that remember, learn, and improve over time
6

Module 6: Planning & Reflection Systems

FROM (Point A)
“Reactive agents (respond to immediate inputs), no strategic planning, trial-and-error execution, no self-improvement”
TO (Point B)
“Proactive agents with planning capabilities, multi-step strategy generation, deliberate execution, self-reflection and improvement”
🛡️ Planning and reflection expertise—building agents that think ahead, strategize, and learn from mistakes
7

Module 7: Code Generation & Self-Improvement

FROM (Point A)
“Agents that can't modify code, static tool sets, manual feature additions, limited adaptability”
TO (Point B)
“Code-generating agents, self-improving capabilities, dynamic tool creation, adaptive agents”
🛡️ Code generation expertise—building agents that write their own tools and extend their own capabilities
8

Module 8: Multi-Agent Coordination

FROM (Point A)
“Single-agent limitations (one agent does everything), monolithic agent systems, no specialization, poor scalability”
TO (Point B)
“Multi-agent systems with coordination, specialized agents, parallel execution, scalable agent teams”
🛡️ Multi-agent architecture mastery—building systems where multiple specialized agents collaborate to solve complex problems
9

Module 9: Production Deployment & Monitoring

FROM (Point A)
“Local-only agents, no production deployment, no monitoring, unpredictable production behavior”
TO (Point B)
“Production-deployed agents, full observability, performance monitoring, reliable production systems”
🛡️ Production deployment mastery—operating autonomous agent systems at scale with reliability and continuous improvement

The Shu-Ha-Ri Learning Method

Ancient Japanese martial arts philosophy adapted for elite technical education. Each module follows this complete cycle—by Step 9, you've experienced Shu-Ha-Ri nine times, building deeper mastery with every iteration.

📚

Shu (守) - Learn

TedTalk-style masterclass + guided hands-on coding

“Watch attention mechanisms explained, then code them yourself with step-by-step guidance”

🔨

Ha (破) - Break

Modify code, experiment with parameters, adapt to your problems

“Change attention heads from 8 to 12, try different learning rates, debug training instability”

🚀

Ri (離) - Transcend

Apply independently, innovate beyond what's taught

“Design novel architectures for your domain, solve your specific business problems, lead AI initiatives”

This is how you transcend from passive learner to active innovator. This is executive business education merged with hands-on mastery.

Proven Transformation Results

Real outcomes from students who completed The Agent Architecture Blueprint™ and built production agent systems

📈 Career Transformation

75%
Promoted to Senior+ within 12 months
$80K-$150K
Average salary increase
90%
Report being 'irreplaceable' at their company
85%
Lead AI initiatives after completion

💰 Business Impact

$150K/year
Average API cost savings from owning model weights
70%
Eliminate third-party model dependencies entirely
60%
Raise funding citing proprietary technology as moat
3-6 months
Average time to ROI on course investment

What You'll Actually Build

🏗️
Complete GPT
4,000+ lines of PyTorch
🧠
Attention
From scratch, no libraries
📊
Training
100M+ tokens
🎯
Classification
95%+ accuracy
💬
ChatBot
Instruction-following

Choose Your Path to Mastery

All modalities include the complete Agent Architecture Blueprint™. Choose based on your learning style and goals.

Self-Paced Mastery

$1,997
Lifetime Access
Self-directed learners
  • All 9 modules available immediately (45+ hours)
  • Lifetime access to content and updates
  • Private Discord community (peer support)
  • Monthly live office hours with Q&A
  • 9 production system templates (starter code)
  • GitHub repository with all implementations
  • Certificate of completion
  • Learn on your own schedule
Most Popular

9-Week Live Cohort

$6,997
12 Weeks
Engineers wanting accountability
  • Weekly live workshops with Dr. Lee (3 hours each)
  • Cohort accountability and peer learning
  • Direct instructor access (24-hour response)
  • Weekly code reviews (instructor + peer feedback)
  • Pair programming sessions
  • Career advancement coaching
  • Alumni network access
  • Graduation certificate
  • Job board access (companies hiring agent engineers)
  • Fixed start dates (4 cohorts per year)

Founder's Edition

$19,997
6 Months
Founders & technical leaders
  • One-on-one implementation partnership
  • Weekly architecture sessions (2 hours private)
  • Custom curriculum for YOUR specific use case
  • Hands-on pair programming (we code together)
  • Production deployment support
  • 90 days of fractional CTO access
  • Architecture design document (custom)
  • Code review (every pull request)
  • Team training (upskill your engineers)
  • Three tiers: Solo Founder ($19,997), CTO + Team ($34,997), Enterprise ($49,997)

5-Day Immersive Bootcamp

Executive format: Monday-Friday intensive (8am-6pm). Build complete GPT in one week. Limited to 15 participants for maximum attention.

Course Curriculum

10 transformative steps · 40 hours of hands-on content

1

Module 1: What Are LLM Agents?

6 lessons · Shu-Ha-Ri cycle

  • Executive Overview: Why Agents Create Business Value Beyond Chatbots
  • Defining LLM Agents: From Pattern Matching to Autonomous Action
  • Use Cases: Report Generation, Web Search, Coding Agents, Computer Use
  • Prerequisite LLM Capabilities: Tool Use, Reasoning, and Instruction Following
  • The Processing Loop: How Agents Think, Decide, and Act
  • Build vs. Buy: When Custom Agents Create Competitive Advantage
2

Module 2: Working with Tools

7 lessons · Shu-Ha-Ri cycle

  • The Tool Abstraction: Why Agents Need a Consistent Interface
  • Implementing ToolCall and ToolCallResult Data Structures
  • Building BaseTool: A Blueprint for All Agent Tools
  • SimpleFunctionTool: Wrapping Python Functions as Tools
  • PydanticFunctionTool: Type-Safe Tool Inputs with Validation
  • Async Tool Patterns: Non-Blocking Tool Execution
  • Hands-On: Building Your First Working Tool
3

Module 3: Working with LLMs

7 lessons · Shu-Ha-Ri cycle

  • The LLM Abstraction: Why Provider Independence Matters
  • Implementing ChatMessage and ChatRole Structures
  • Building BaseLLM: A Blueprint for LLM Interfaces
  • OllamaLLM: Connecting to Local Models
  • OpenAI and Claude Adapters: Cloud Provider Integration
  • Tool Calling with LLMs: The Complete Flow
  • Hands-On: Hailstone Tool Call with Your LLM Interface
4

Module 4: The LLM Agent Class

6 lessons · Shu-Ha-Ri cycle

  • Agent Architecture: Bringing Tools and LLMs Together
  • The Agent Loop: Observe, Think, Act, Repeat
  • Managing Conversation History and Context Windows
  • Tool Selection: How Agents Choose Which Tool to Use
  • Error Handling: What Happens When Tools Fail
  • Building the Complete Agent Class from Scratch
5

Module 5: Capstone—Your First Working Agent

6 lessons · Shu-Ha-Ri cycle

  • Designing a Research Agent: Requirements and Architecture
  • Implementing Web Search and Content Extraction Tools
  • The Agent in Action: Step-by-Step Execution Walkthrough
  • Debugging Agent Behavior: Understanding Decision Points
  • Extending Your Agent: Adding New Capabilities
  • Performance Analysis: What Works and What Doesn't
6

Module 6: Making Agents MCP Compatible

6 lessons · Shu-Ha-Ri cycle

  • The Model Context Protocol: Why Standards Matter
  • MCP Architecture: Servers, Clients, and Tool Discovery
  • Connecting Your Agent to the MCP Ecosystem
  • Implementing MCP-Compatible Tool Interfaces
  • Resource Management: Files, Databases, and APIs
  • Testing MCP Compatibility
7

Module 7: Capstone—Deep Research with MCP

6 lessons · Shu-Ha-Ri cycle

  • Building a Deep Research Agent: Multi-Source Information Gathering
  • Integrating Multiple MCP Servers
  • Orchestrating Complex Research Workflows
  • Synthesizing Information Across Sources
  • Generating Structured Research Reports
  • Evaluation: Measuring Research Quality
8

Module 8: Human-in-the-Loop Patterns

6 lessons · Shu-Ha-Ri cycle

  • When Agents Need Human Oversight: High-Stakes Decisions
  • Approval Patterns: Pause, Review, and Continue
  • Confirmation Interfaces: Designing for Human Review
  • Escalation Logic: When to Ask vs. When to Act
  • Audit Trails: Recording Agent Decisions for Review
  • Implementing Human-in-the-Loop in Your Agent
9

Module 9: Memory Modules

6 lessons · Shu-Ha-Ri cycle

  • Why Agents Need Memory: State Across Tasks and Sessions
  • Short-Term Memory: Conversation Context and Working Memory
  • Long-Term Memory: Persistent Knowledge and Learning
  • Memory Architectures: Vector Stores, Key-Value, and Hybrid
  • Implementing Memory Modules for Your Agent
  • Sharing State Across Tasks: Session Management
10

Module 10: Production Deployment and Evaluation

6 lessons · Shu-Ha-Ri cycle

  • Evaluating Agent Performance: Task Completion Metrics
  • Benchmarking on Real-World Tasks
  • Cost Analysis: LLM Calls, Tool Execution, and Optimization
  • Monitoring Agent Behavior in Production
  • Scaling Agent Workloads
  • Capstone: Your Complete Production-Ready Agent

Production-Grade Tech Stack

Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems

For Career Advancers

I help AI engineers and ML practitioners build production-ready autonomous agent systems from first principles, so they can deploy reliable multi-agent architectures earning $200K-$400K salaries without being commoditized as framework users or stuck debugging black-box systems they don't understand.

For Founders & CTOs

I help technical founders and CTOs build proprietary autonomous agent infrastructure from first principles, so they can raise Series A at 2-3x premium valuations with defensible moats without burning $10K-$500K/month on APIs or being dismissed by VCs as 'just an API wrapper.'

PythonOllamaOpenAI APIClaude APIPydanticasyncioMCP

Frequently Asked Questions

Do I need to use LangChain or other frameworks?

No. You'll build everything from scratch—the processing loop, tool abstractions, LLM interfaces, and memory modules. This gives you deeper understanding and complete control over your agent architecture.

What hardware do I need?

Everything runs on a standard laptop. We use Ollama for local models and provide cloud API integration for OpenAI and Claude. No specialized hardware required.

How is this different from prompt engineering?

Prompt engineering optimizes what you ask the LLM. Agent engineering builds the infrastructure around the LLM—tool calling, memory, loops, and orchestration—that enables autonomous task execution.

Will I build something that actually works?

Yes. You'll build a complete research agent that can search the web, extract content, synthesize information, and generate reports—all running on your own machine.

What's the business case for building custom agents?

Custom agents automate complex workflows that chatbots can't handle. They reduce manual work, operate 24/7, and can be tailored exactly to your business processes—creating defensible competitive advantage.

Do I need to take other courses first?

Python proficiency and basic LLM familiarity are required. The LLM course is helpful but not required—we include enough context to get started.

Stop Renting AI. Start Owning It.

Join 500+ engineers and founders who've gone from API consumers to model builders—building their competitive moats one step at a time.

Command $250K-$400K salaries or save $100K-$500K in annual API costs. Own your model weights. Build defensible technology moats. Become irreplaceable.

Starting at
$1,997

Self-paced · Lifetime access · 30-day guarantee

Start Your Transformation

This is not just education. This is technological sovereignty.

30-day guarantee
Lifetime updates
Zero API costs forever