8.9 KiB

Raw Permalink Blame History

Slide Deck Outlines

Presentation outlines for all 8 modules. Each module has ~10-15 slides.

Module 1: Foundations (~12 slides)

Slide	Title	Content
1	Title	Agentic Engineering: From Foundations to Production
2	What Makes an Agent?	LLM + Tools + Loop = Agent. Diagram of the 3 components.
3	The Harness vs The Model	Table comparing what matters. "The harness IS the product."
4	5 Subsystems of a Harness	Instructions, Tools, Environment, State, Verification. Diagram.
5	4 Dimensions of Control	Context, Model, Prompt, Tools. Which gives you leverage?
6	The Repository IS the Spec	Single source of truth. CLAUDE.md, init.sh, feature_list.json.
7	Decision Framework	4-question filter. When to use agents, when not to.
8	The Agent Loop	Think → Act → Observe → Repeat. Pseudocode.
9	Tool Calling Deep Dive	Anatomy of a tool. The reasoning parameter. Forced vs auto.
10	Vibe Coding vs Agentic Engineering	5 hard rules. Risk compounds with runtime.
11	Do You Trust Your Agents?	The course thesis. Discussion question.
12	Module Summary	Key takeaways. Prep for Lab 1.8.

Module 2: Architecture (~15 slides)

Slide	Title	Content
1	Title	Agent Architecture
2	The Four Pillars	Tools, Loop, Context, Memory. Diagram.
3	Tool Design Patterns	MCP vs CLI vs Script vs Skills. Context cost matrix.
4	LSP MCP Server	Symbol-level search. Diagram. When grep is too slow.
5	Agent Loop Variants	5 levels: Simple → Tool-Use → Task-Completion → Sub-Agent → Multi-Agent
6	Skills System Deep Dive	Path-scoped skills, subdirectory CLAUDE.md, plugin distribution.
7	Agent-Readable Workspace	init.sh, feature_list.json, clean state. The discovery pattern.
8	Agent Loop Variants Detail	Code examples for each level.
9	Context Window Management	Sliding window, summarization, structured outputs. Hybrid approach.
10	Memory Patterns	Mental models, expertise files, scratch pads, session logs.
11	The Reasoning Parameter	Force chain-of-thought on every tool call. Code example.
12	Codebase Architectures	Atomic, Layered, Pipeline, Vertical Slice. Comparison.
13	Architecture Decision Tree	Which pattern for which project?
14	Module Summary	Key takeaways. Prep for labs.

Module 3: Safety & Security (~14 slides)

Slide	Title	Content
1	Title	Safety & Security
2	Prompt Injection (L0)	ACIP defense. Attack vectors: direct, indirect, role-play.
3	Why Bash Is the Problem	Single funnel for every destructive action. Diagram.
4	The Probability Math	1% × 100 turns = 63%. The chart that changes everything.
5	The 6-Level Security Ladder	L0 (ACIP) → L1 (skill) → L2 (system prompt) → L3 (blacklist) → L4 (whitelist) → L5 (no bash)
6	L3 Marque Break	Agent writes cleanup.py → runs python cleanup.py. The demo.
7	Damage Control	3 access levels: zero-access, read-only, no-delete. YAML config.
8	Hook Architecture	13 lifecycle events. PreToolUse, PostToolUse, Stop, etc.
9	The Verifier Pattern	Builder + Verifier over Unix socket. Diagram.
10	Confidence Ladder	PERFECT → VERIFIED → PARTIAL → FEEDBACK → FAILED
11	Defense-in-Depth Stacking	All 6 levels stacked. No single point of failure.
12	Security Decision Tree	Which level for which deployment?
13	Module Summary	Key takeaways. Prep for labs.

Module 4: Multi-Agent Orchestration (~18 slides)

Slide	Title	Content
1	Title	Multi-Agent Orchestration
2	Why One Agent Is Not Enough	Context ceiling, capability ceiling, reliability ceiling.
3	3 Orchestration Patterns	Dispatcher, Pipeline, P2P. Comparison diagram.
4	P-Threads	N agents in parallel. Pick best result. mprocs example.
5	F-Threads	N agents → Judge → Winner. Your UI Agents system.
6	Depth-2 Delegation	Orchestrator → Leads → Workers. Never execute.
7	Agent Experts That Remember	Mental models. Self-improve commands. Compounding knowledge.
8	Domain Locking	Per-agent permissions. Read/write/delete scope.
9	TillDone Task Discipline	Task list gating. Live progress. Nudge on incomplete.
10	Agent Chains	YAML-defined pipelines. $INPUT/$ORIGINAL variables.
11	P2P Communication	Pi-to-Pi. 4 tools. Hop limits. Self-healing.
12	MCP Agent Mail	Jeff's alternative: inboxes, threads, file leases.
13	Service Connectors	Twitter, GitHub, Linear, Slack via MCP. One per service.
14	Conversation Awareness	Shared JSONL logs. Cross-agent context.
15	CEO Board System	8 agents. Adversarial debate. Fact verification.
16	UI Agents System	12 agents. Brand → Product → Tree → Branch → Leaf.
17	Orchestration Decision Tree	Which pattern for which problem?
18	Module Summary	Key takeaways. Prep for labs.

Module 5: Production Patterns (~14 slides)

Slide	Title	Content
1	Title	Production Patterns
2	Production Is Different	Deterministic vs non-deterministic. Fixed vs variable cost.
3	CI/CD for Agents	Golden datasets. Regression testing. Prompt versioning.
4	The 5-Tool Production Stack	Your architecture: Claude Code, Pi, OpenCode, Hermes, OpenClaw.
5	Stack Diagram	agent-mux → mprocs → psmux → dmux. Full stack.
6	The Agent Manager Role	5 areas of ownership. 90-day playbook.
7	Shadow Deployments	Run new agent alongside prod. Compare before switching.
8	Rollback Strategies	Prompt + model + parameters + config. Version-locked configs.
9	Observability	SQLite tracing. Every tool call logged. Decision tracing.
10	Cross-Provider Session Search	Jeff's tool. Index sessions across 11 providers.
11	Alerting	Agent-specific signals: tool loops, cost spikes, grinding. P0/P1/P2 routing.
12	Deployment Modes	Local trusted, authenticated private, authenticated public.
13	Cost Control	Budgets per agent/session/task. Warnings vs hard stops.
14	Module Summary	Key takeaways. Prep for labs.

Module 6: Economics & Evaluation (~12 slides)

Slide	Title	Content
1	Title	Economics & Evaluation
2	The Compute Advantage Equation	(Compute Scaling × Autonomy) ÷ (Time + Effort + Cost).
3	Your Stack's Compute Advantage	Real numbers for each tool in your stack.
4	LLM Pricing Landscape	100x range. Gemini Flash ($0.15/M) to Claude Opus ($15/$75/M).
5	Cascade Routing	Cheap model for simple steps, expensive for complex. 66% savings.
6	Cost Per Session Math	The 3x rule. Retries, tool overhead, context growth.
7	Jeff Emanuel Case Study	52 subs, $12K/mo, 85K commits. What's his CAC?
8	Agent Evaluation Metrics	pass@k, tool call accuracy, cost per task, loop efficiency.
9	Automated Evaluation	Golden datasets. VCR-style recording. Regression detection.
10	A/B Testing Agents	Canary deployments. 5% traffic split. Compare success rate, cost.
11	Human Evaluation	Spot-check 5-10%. Catches 2-3x more failures than automated.
12	Module Summary	Key takeaways. Prep for labs.

Module 7: Advanced Topics (~10 slides)

Slide	Title	Content
1	Title	Advanced Topics
2	Autoresearch	Run → Measure → Log → Decide. Self-improving agents.
3	Integrity Guards	Code hashing (anti-grinding). Median over best. Test set integrity.
4	Meta-Agents	Agents that build agents. Parallel research experts.
5	Beyond MCP	MCP vs CLI vs Script vs Skills. Context cost matrix.
6	Mac Mini Agent	Physical sandbox. Steer + Drive + Listen + Direct.
7	Always-On Agents	Voice→CLI bridge. Job server pattern. Heartbeat execution.
8	Framework Comparison	Paperclip vs Multica vs Cabinet. When to use which.
9	The Future	Agentic Coding 2.0. Agents conducting agents.
10	Module Summary	Key takeaways. Prep for labs.

Module 8: Capstone (~8 slides)

Slide	Title	Content
1	Title	Capstone: Production Multi-Agent System
2	Project Options	Brand Monitor, Code Review Pipeline, Strategic Decision Board.
3	Phase 1: System Design	Architecture diagram. Agent roster. Data model. Cost estimate.
4	Phase 2-3: Build + Integrate	Agent implementation. Mental models. Wiring agents together.
5	Phase 4-5: Security + Test	L3 minimum. Golden dataset. pass@k evaluation.
6	Phase 6-7: Deploy + Review	CI/CD. Shadow deploy. Red-team. Retrospective.
7	Pass Criteria	What constitutes a passing project. Rubric.
8	Next Steps	After the capstone. Continuing education. Community.