# 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 directory ``` ### Pi 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 orchestration ``` ### OpenCode ```bash opencode run "task" # Headless execution opencode --model opencode-go/deepseek-v4-flash run # Specific model opencode --config ~/.grok/config.toml run # Custom config ``` ### OpenClaw ```bash claw start --daemon # Always-on employee claw do "task description" # One-shot execution claw schedule --cron "0 6 * * 1" --task # Recurring task ``` ### Install Course ```bash # 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 ```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 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 ```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 × 1.5 ``` ### 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 ```