agentic-ai-engineering/course/INSTRUCTOR-GUIDE.md

Instructor Guide

Teaching notes, timing, discussion questions, common mistakes, and lab troubleshooting for each module.

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Module 1: Foundations (4-6 hours)

Timing Guide

Section	Time	Format
Lesson 1.1-1.3	60 min	Lecture + discussion
Lesson 1.3a-1.4	40 min	Lecture + demo
Lesson 1.5-1.6	50 min	Lecture + live coding
Lesson 1.7 (Trust)	15 min	Discussion
Lab 1.8	60 min	Hands-on
Quiz	15 min	Individual

Key Discussion Questions

1. "What's the difference between vibe coding and agentic engineering?" — Lead with the 5 hard rules from mythos-learnings.md
2. "When should you NOT use an agent?" — Walk through the decision framework table
3. "Do you trust your agents?" — This is the course thesis. Get students to share their trust level.

Common Mistakes

• Confusing the model with the agent: Students think "better model = better agent." Emphasize the harness.
• No iteration limits: Students build loops without max iterations. Always set MAX_ITERATIONS.
• Skipping the reasoning parameter: Students define tools without reasoning fields. Always add it.

Lab Troubleshooting

• API key not set: The mock LLM client handles this. If they want real API, ensure ANTHROPIC_API_KEY is set.
• File not found errors: Labs expect files in the current directory. cd to the lab folder first.

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Module 2: Architecture (6-8 hours)

Timing Guide

Section	Time	Format
Lessons 2.1-2.3	75 min	Lecture
Lessons 2.4-2.6	75 min	Lecture + demo
Lessons 2.7-2.8	50 min	Architecture discussion
Lab 2.9-2.10	90 min	Hands-on (split across 2 labs)
Quiz	15 min	Individual

Key Discussion Questions

1. "Which codebase architecture fits your project?" — Compare atomic vs layered vs pipeline vs vertical slice
2. "How do you handle context overflow in practice?" — Real examples from your tac/ projects
3. "What's the most common memory mistake?" — Agents that don't persist expertise between sessions

Common Mistakes

• One tool to rule them all: Students try to make one tool do everything. Enforce single-responsibility.
• No output limits: Tool results overflow context. Cap returns at 2KB for logs, 10 items for search.
• Flat hierarchy for complex agents: Students skip domain locking. Add it early.

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Module 3: Safety & Security (5-7 hours)

Timing Guide

Section	Time	Format
Lesson 3.1 (ACIP + Bash)	45 min	Lecture + probability math demo
Lessons 3.2-3.5	90 min	Technical deep dive
Lessons 3.6-3.7	60 min	Architecture + stacking
Labs 3.8-3.9	90 min	Hands-on
Quiz	15 min	Individual

Key Discussion Questions

1. "Have you ever had an agent do something destructive?" — Share war stories. The L3 marquee break usually gets reactions.
2. "What level of security do you run?" — Most students are at L1-L2. Show them the math (1% × 100 turns = 63%).
3. "Would you trust a verifier agent?" — This is where trust becomes concrete. Demo the verifier pattern.

The Probability Math Demo

# Show this live:
for p in [0.01, 0.001, 0.0001]:
    for n in [10, 50, 100, 1000]:
        prob = 1 - (1-p)**n
        print(f"p={p:.4f}, n={n}: {prob:.1%} failure rate")

Lab Troubleshooting

• Whitelist too restrictive: Students block legitimate commands. Start with 10 patterns, add more as needed.
• Verifier has no tools: Students forget to give the verifier read tools. Check tool surface.
• Socket connections: The verifier lab uses Unix sockets. Windows users need WSL.

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Module 4: Multi-Agent Orchestration (7-9 hours)

Timing Guide

Section	Time	Format
Lessons 4.1-4.4	90 min	Architecture + patterns
Lessons 4.5-4.9	90 min	Technical deep dive
Lessons 4.10-4.11	60 min	Case study (CEO Board, UI Agents)
Labs 4.12-4.13	90 min	Hands-on
Quiz	15 min	Individual

Key Discussion Questions

1. "One agent or many?" — Walk through the decision framework. When does multi-agent make sense?
2. "Orchestrator should never execute" — This is the hardest rule for students. They want the orchestrator to do work.
3. "Flat vs hierarchical?" — Compare P2P coms vs depth-2 delegation. Which fits your use case?

Common Mistakes

• Orchestrator does the work: The orchestrator should delegate, not code. Enforce this with domain permissions.
• No mental models: Agents forget everything between sessions. Always include a mental model file.
• No domain locks: Agents step on each other's files. Always add domain permissions.
• P2P without hop limits: Agents loop forever. Always set MAX_HOPS=5.

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Module 5: Production Patterns (5-7 hours)

Timing Guide

Section	Time	Format
Lessons 5.1-5.4	90 min	Lecture + case study
Lessons 5.5-5.8	90 min	Technical deep dive
Labs 5.9-5.10	90 min	Hands-on
Quiz	15 min	Individual

Key Discussion Questions

1. "What does production mean for agents?" — Different from traditional software. Non-deterministic behavior changes everything.
2. "Have you hit a cost surprise?" — Share stories. The 3x rule usually resonates.
3. "Shadow deployment or not?" — When is shadow worth the complexity?

The 5-Tool Stack Demo

Show your actual mprocs config (~/mprocs-teams.yaml) and explain:

• Why Claude Code is the lead
• Why Pi is the customizable harness
• Why OpenCode is the OSS backup
• How agent-mux ties it together

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Module 6: Economics & Evaluation (4-6 hours)

Timing Guide

Section	Time	Format
Lessons 6.0-6.2	60 min	Lecture
Lessons 6.3-6.5	60 min	Technical
Lessons 6.6-6.7	40 min	A/B testing + human eval
Labs 6.8-6.9	75 min	Hands-on
Quiz	15 min	Individual

Key Discussion Questions

1. "What's your current agent spend?" — Use the Compute Advantage Equation to calculate their leverage.
2. "Do you measure agent performance?" — Most teams don't. Show pass@k and cost per task.
3. "Would you trust an eval harness?" — The golden dataset approach. Get students to create one for their project.

Jeff Emanuel Case Study

Share Jeff's numbers: 52 subs at $12K/mo, 85K commits/year. Ask: "What's his Compute Advantage? How would cascade routing change his costs?"

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Module 7: Advanced Topics (5-7 hours)

Timing Guide

Section	Time	Format
Lessons 7.1-7.3	75 min	Lecture + demo
Lessons 7.4-7.6	75 min	Architecture
Labs 7.7-7.8	90 min	Hands-on
Quiz	15 min	Individual

Key Discussion Questions

1. "Should we trust agents to improve themselves?" — The autoresearch loop. Integrity guards are essential.
2. "What can't MCP do well?" — Beyond MCP: context cost trade-off matrix.
3. "Are we building our own replacement?" — The meta-agent exercise gets existential. Good discussion.

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Module 8: Capstone (8-12 hours)

Teaching Approach

This is self-directed. Students pick one of three projects and work through 7 phases. The instructor's role:

1. Phase 1 (Design): Review architecture diagrams. Check that agents have clear roles and domain permissions.
2. Phase 3 (Integration): Check that agents can actually communicate. Common point of failure.
3. Phase 5 (Testing): Help students create golden datasets. This is where most learning happens.
4. Phase 7 (Review): Facilitate a retrospective session. Students present what broke.

Grading

Use the rubric in M8-CAPSTONE.md. Key pass criteria:

• System runs without manual intervention
• All agents have domain-locked permissions
• Each agent has a mental model file
• pass@k > 60%
• Cost analysis within 2x of optimal

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Overall Teaching Tips

Pace

• Don't let students spend more than 15 min on any single TODO in a lab
• If stuck, give them the next 3 lines of the solution, not the whole thing
• Labs are designed to fail early — celebrate failures as learning moments

Audience Adaptation

• Technical audience: Focus on labs and code. Speed through theory.
• Non-technical audience: Use NON-TECHNICAL.md as primary material. Skip most labs.
• Mixed audience: Split into pairs (technical + non-technical) for labs.

Environment Setup

Before the course starts, ensure students have:

1. Python 3.12+ installed
2. pip install anthropic (or they use mock LLM)
3. VS Code or any editor
4. Git
5. Optional: Claude Code, Pi Agent, or OpenCode CLI

Handling Questions

• "Which model should I use?" → M6 pricing table. Cascade routing decision tree.
• "Is this safe?" → M3 security ladder. Always start at L3 minimum.
• "How do I sell this to my boss?" → NON-TECHNICAL.md. Compute Advantage Equation.
• "What about [framework]?" → Our course is framework-agnostic. The patterns apply everywhere.