Teaching & Training Layer

Learning Paths & Course Tracks

Three guided paths by skill level, plus five role-based course tracks.

Guided Learning Paths

Start Here — Pick Your Level

Beginner~30 minutes

Understanding AI Agents

Start here if you're new to agent engineering. Learn what makes Claude Code different from a chatbot, and understand the core patterns.

01📖Why Learn Claude — context & purposeread02📖Timeline — what happened and why it mattersread03🔬Tool Architecture — what an agent loop isexplore04🔬autoDream — how memory changes everythingexplore05🔬BUDDY — how AI systems create engagementexplore06📖Glossary — key terms referenceread
Intermediate~1 hour

Agent Architecture Deep Dive

For engineers who want to understand the full architecture. Covers memory, hooks, subagents, context management, and multi-agent coordination.

01🔬Three-Layer Memory Architectureexplore02🧪Memory Lab — hands-on practicelab03🔬Context Entropy — 5 compaction strategiesexplore04🔬Coordinator Mode — multi-agent patternsexplore05🔬Advisor Agent — quality through supervisionexplore06🧪Hook Lab — standards as automationlab07🔬Feature Flags — staged deploymentexplore08📖Ecosystem Mapping — translate to your stackread
Advanced~2 hours

Production Agent Systems

For architects building production agent systems. Covers always-on daemons, cloud planning, security patterns, and governance.

01🔬KAIROS — always-on daemon architectureexplore02🔬ULTRAPLAN — cloud deep planningexplore03🔬UDS Inbox — inter-agent communicationexplore04🔬Anti-Distillation — defensive engineeringexplore05🔬Undercover Mode — transparency governanceexplore06🔬Model Codenames — performance regressionexplore07📖Governance framework — V5.3 complianceread08🔬Full Capability Index — all 14 discoveriesexplore
Role-Based Tracks

5 Course Tracks

🏛️

Founder Track

planned
Founders, ecosystem architects, strategic decision makers

Why agent engineering matters strategically. Governance, leverage, and platform direction.

Topics
01Why agent engineering changes the game
02Strategic implications of always-on AI systems
03Governance as competitive advantage
04Platform vs. tool: the Claude Code lesson
05Building agent-native organizations
🔨

Builder Track

planned
Engineers, AI workflow builders, platform developers

Hands-on deep dive into memory, hooks, skills, subagents, and agent harnesses.

Topics
013-layer memory architecture
02Lifecycle hooks and quality gates
03Skill packaging and distribution
04Subagent patterns: Fork, Teammate, Worktree
05Context compaction strategies
06Multi-agent coordination (Coordinator Mode)
07NightOps and background daemon design
08Tool architecture and permission models
⚙️

Operator Track

planned
Claude Code users, workflow operators, project managers

Workflow execution, prompt discipline, maintaining project rules, using skills and memory effectively.

Topics
01Setting up project memory correctly
02Writing effective CLAUDE.md rules
03Using hooks for operational quality
04Skill invocation and customization
05Session management and context hygiene
06Working with subagents effectively
📚

Student-Friendly Track

planned
Students, AI learners, curious beginners

What AI agents are, why memory matters, how reusable workflows work, how systems learn from repetition.

Topics
01What are AI agents? (beyond chatbots)
02Why does memory change everything?
03How reusable workflows save time
04What 'always-on' AI means
05How AI systems learn from repetition
06The ethics of AI transparency
🛡️

Governance Track

planned
Compliance, audit, risk management, policy makers

Lawful study boundaries, V5.3 mapping, permissions, quality gates, operational risk control.

Topics
01Lawful study boundaries in AI research
02V5.3 governance framework mapping
03Permission models and access control
04Quality gate implementation
05Operational risk in agent systems
06Anti-distillation and IP protection awareness
07Attribution policy for AI-generated content