Agentic Engineering — Technical Field Manual
A consolidated quick reference for all course concepts, commands, and patterns.
1. Agent CLI Quick Reference
Claude Code
bash
claude --init # Project setup
claude --teammate-mode tmux # Multi-agent teams (split panes)
claude -p "prompt" --print # Headless execution
claude --allowedTools "Read Write" # Constrain tool surface
claude --hooks .claude/hooks/ # Custom hooks directoryPi Coding Agent
bash
pi -e extensions/damage-control.ts # Security auditing
pi -e extensions/tilldone.ts # Task discipline
pi -e extensions/coms.ts # P2P agent communication
pi --mode rpc # Programmatic control (26+ commands)
pi -e extensions/agent-team.ts # Dispatcher orchestration
pi -e extensions/agent-chain.ts # Pipeline orchestrationOpenCode
bash
opencode run "task" # Headless execution
opencode --model opencode-go/deepseek-v4-flash run # Specific model
opencode --config ~/.grok/config.toml run # Custom configOpenClaw
bash
claw start --daemon # Always-on employee
claw do "task description" # One-shot execution
claw schedule --cron "0 6 * * 1" --task # Recurring taskInstall Course
bash
# Windows
powershell -File install.ps1
# Linux/Mac
bash install.sh
bash install.sh security # Single kit2. The 6-Level Security Ladder
L0: ACIP (prompt injection defense) — Costs nothing, blocks simple attacks
L1: safe-mode skill — Theatre (model can override)
L2: --append-system-prompt — Theatre+ (model can still override)
L3: Bash blacklist hook — Reactive (use as FLOOR)
L4: Bash whitelist hook — Architectural (only N safelisted cmds)
L5: No bash, custom tools only — Production-grade (bash doesn't exist)Probability Math
python
P_failure = 1 - (1 - p)^N
# p=0.01 (1%), N=100: 63.4% chance of disaster
# p=0.001 (0.1%), N=100: 9.5%
# p=0.0001 (0.01%), N=1000: 9.5%Damage Control — 3 Access Levels
yaml
zeroAccessPaths: [".env", "~/.ssh/", "*.pem"] # Can't read/write
readOnlyPaths: ["package-lock.json", "node_modules/"] # Can read only
noDeletePaths: [".git/", "Dockerfile"] # Can modify, can't delete3. Orchestration Patterns
| Pattern | Structure | Best For | Tool |
|---|---|---|---|
| P-Thread | N agents parallel, pick best | Creative work, benchmarking | mprocs |
| F-Thread | N agents → Judge → Winner | Code gen, UI gen | agent-team |
| B-Thread | Orchestrator → Leads → Workers | Production systems | lead-agents |
| C-Thread | Agent → Checkpoint → Human | High-stakes work | tilldone |
| L-Thread | Long-running hours+ | Background tasks | OpenClaw daemon |
| Agent Chain | Step1 → Step2 → Step3 | Plan→Build→Review | agent-chain |
YAML Pipeline Template
yaml
steps:
- agent: planner
prompt: "Create plan for: $INPUT"
- agent: builder
prompt: "Implement: $INPUT"
- agent: reviewer
prompt: "Review: $INPUT"4. Key Formulas
Compute Advantage
CA = (Compute Scaling × Autonomy) ÷ (Time + Effort + Monetary Cost)pass@k
pass@k = 1 - (1 - p)^k
# p=0.6 (60% single), k=3: 93.6%
# p=0.6, k=5: 98.9%3x Cost Rule
Production cost = Prototype cost × 3.0
With retries = Prototype cost × 1.5Cascade Routing Savings
Savings = 1 - (cascade_cost / single_model_cost)
# Typical: 66-80% savings5. Model Pricing (per million tokens)
| Model | Input | Output | Best For |
|---|---|---|---|
| Gemini 2.5 Flash | $0.15 | $0.60 | High-volume, simple |
| DeepSeek V3 | $0.27 | $1.10 | Structured tasks |
| Claude Sonnet 4 | $3.00 | $15.00 | General agentic |
| Claude Opus 4 | $15.00 | $75.00 | Complex planning |
| GPT-5 | $10.00 | $40.00 | Frontier reasoning |
Cascade Routing Strategy
Simple retrieval → Gemini Flash ($0.15/$0.60)
Analysis → Claude Sonnet ($3/$15)
Critical decision → Claude Opus ($15/$75)
Formatting → Gemini Flash ($0.15/$0.60)6. Verifier Confidence Ladder
| Level | Meaning | Bar Color |
|---|---|---|
| PERFECT | Every claim verified, zero gaps | Green |
| VERIFIED | All passed, minor non-blocking gaps | Green |
| PARTIAL | No failures, significant unverifiable gaps | Orange |
| FEEDBACK | At least one claim failed, correction sent | Orange |
| FAILED | Cannot verify, escalating to human | Red |
7. Lab Index
| Lab | Module | Topic | Files |
|---|---|---|---|
| L1 | M1 | First Agent (single-tool) | starter.py, solution.py |
| L2a | M2 | Multi-Tool Agent | starter.py, solution.py |
| L2b | M2 | Context-Aware Agent | starter.py, solution.py |
| L3a | M3 | Whitelist Hook (L4) | starter.py, solution.py |
| L3b | M3 | Verifier Agent | starter.py, solution.py |
| L4a | M4 | Agent Chain (YAML) | starter.yaml, solution.yaml |
| L4b | M4 | Multi-Team Config | starter.yaml, solution.yaml |
| L5a | M5 | Observability (SQLite) | starter.py, solution.py |
| L5b | M5 | CI/CD Pipeline | starter.py, solution.py |
| L6a | M6 | Eval Harness (pass@k) | starter.py, solution.py |
| L6b | M6 | Cost Optimization | starter.py, solution.py |
| L7a | M7 | Autoresearch Loop | starter.py, solution.py |
| L7b | M7 | Meta-Agent | starter.py, solution.py |
Run offline: python -c "from mock_llm import MockAnthropic; print('Mock LLM ready')"
8. Capstone Options
| Project | Difficulty | Description | Reference |
|---|---|---|---|
| Brand Monitor | Intermediate | Multi-LLM brand scanning | capstone-reference/brand-monitor/ |
| Code Review Pipeline | Int-Adv | Plan→Build→Review→Verify chain | — |
| Strategic Decision Board | Advanced | 8-agent CEO board | ceo-agents/ |
9. Skill Kits (7 kits, 18 SKILL.md files)
| Kit | Price | Skills | Install |
|---|---|---|---|
| Security Foundation | $49 | L3, L4, L5, damage-control, sandbox | bash install.sh security |
| Multi-Agent Orchestration | $49 | teams, chains, mental-model, domain-lock | bash install.sh orchestration |
| Verifier Pro | $39 | builder, confidence, decomposition | bash install.sh verifier |
| Task Discipline | $29 | tilldone core, progress, nudge, purpose | bash install.sh task |
| Autoresearch | $39 | experiment loop, integrity, median | bash install.sh research |
| Observability | $29 | tracer, cost, replay, loop-detect | bash install.sh observability |
| CEO Board | $49 | 11 agents + verifier + tracker | bash install.sh board |
| Enterprise | $199 | All 7 kits | bash install.sh |
10. The Universal Truth
Deterministic orchestrates non-deterministic. Code is the harness. AI is the engine. Together they're unstoppable.
| Creator | Their Version |
|---|---|
| Kelsey Hightower | "Zero Token Architecture — don't waste tokens on deterministic work" |
| Mario Zechner | "Write architecture by hand. Let agents do the boring stuff" |
| Daniel Miessler | "Code Before Prompts. If bash can do it, don't use AI" |
| IndyDevDan | "ADW — deterministic code orchestrates non-deterministic agents" |
| Anders Hejlsberg | "Don't ask AI for the answer. Ask it to write a program" |
| Armin Ronacher | "Pi's harness layer is worth maintaining carefully because it solves hard problems" |
11. Quick Reference Card
AGENT LOOP: Think → Act → Observe → Repeat
HARNESS: Instructions + Tools + Environment + State + Verification
CONTROL: Context + Model + Prompt + Tools
SECURITY: L0(ACIP) → L1(skill) → L2(prompt) → L3(blacklist) → L4(whitelist) → L5(no-bash)
ORCHESTRATION: Dispatcher | Pipeline | P2P | P-Thread | F-Thread | B-Thread
ECONOMICS: CA = (CS × A) ÷ (T + E + MC) 3x rule Cascade 66-80%
EVALS: pass@k = 1 - (1-p)^k Cost/task Tool call accuracy
TRUST: "Yes, because I've engineered it" — not blind faith