Agentic Engineering — Technical Field Manual
A consolidated quick reference for all course concepts, commands, and patterns.
1. Agent CLI Quick Reference
Claude Code
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 directory
Pi Coding Agent
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 orchestration
OpenCode
opencode run "task" # Headless execution
opencode --model opencode-go/deepseek-v4-flash run # Specific model
opencode --config ~/.grok/config.toml run # Custom config
OpenClaw
claw start --daemon # Always-on employee
claw do "task description" # One-shot execution
claw schedule --cron "0 6 * * 1" --task # Recurring task
Install Course
# Windows
powershell -File install.ps1
# Linux/Mac
bash install.sh
bash install.sh security # Single kit
2. 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
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
zeroAccessPaths: [".env", "~/.ssh/", "*.pem"] # Can't read/write
readOnlyPaths: ["package-lock.json", "node_modules/"] # Can read only
noDeletePaths: [".git/", "Dockerfile"] # Can modify, can't delete
3. 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
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 × 1.5
Cascade Routing Savings
Savings = 1 - (cascade_cost / single_model_cost)
# Typical: 66-80% savings
5. 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