afrexai-agent-engineering

# Agent Engineering — Complete System Design & Operations

Build agents that actually work in production. Not demos. Not toys. Real systems that run 24/7, handle edge cases, and compound value over time.

This skill covers the entire agent lifecycle: architecture → build → deploy → operate → scale.

---

## Phase 1 — Agent Architecture Design

### 1.1 Agent Purpose Definition

Before writing a single line of config, answer these:

```yaml
agent_brief:
  name: ""                    # Short, memorable (max 2 words)
  mission: ""                 # One sentence — what does this agent DO?
  success_metric: ""          # How do you MEASURE if it's working?
  failure_mode: ""            # What does failure look like?
  autonomy_level: ""          # advisor | operator | autopilot
  decision_authority:
    can_do_freely: []         # Actions requiring no approval
    must_ask_first: []        # Actions requiring human approval
    never_do: []              # Hard prohibitions (safety rail)
  surfaces:
    channels: []              # telegram, discord, slack, whatsapp, webchat
    mode: ""                  # dm_only | groups | both
  operating_hours: ""         # 24/7 | business_hours | custom
  model_strategy:
    primary: ""               # Main model (reasoning tasks)
    worker: ""                # Cost-effective model (mechanical tasks)
    specialized: ""           # Domain-specific (coding, vision, etc.)
```

### 1.2 Autonomy Spectrum

Choose deliberately. Most failures come from wrong autonomy level.

| Level | Description | Best For | Risk |
|-------|-------------|----------|------|
| **Advisor** | Suggests actions, human executes | High-stakes decisions, new domains | Low — but slow |
| **Operator** | Acts freely within bounds, asks for anything destructive/external | Most production agents | Medium — good balance |
| **Autopilot** | Broad autonomy, only escalates anomalies | Proven workflows, monitoring tasks | Higher — needs strong guardrails |

**Autonomy Graduat...

# Agent Engineering — Complete AI Agent System Design & Operations

Build production AI agents that actually work. Not demos. Not toys. Real systems that run 24/7, handle edge cases, and compound value over time.

## What This Skill Does

Complete methodology for the entire agent lifecycle:

- **Architecture Design** — Agent purpose, autonomy levels, personality, architecture patterns (solo → swarm)
- **Memory Systems** — 5-layer memory architecture, templates, maintenance protocols
- **Workspace Generation** — Production-ready SOUL.md, AGENTS.md, IDENTITY.md, USER.md, HEARTBEAT.md templates
- **Multi-Agent Teams** — Role matrix, communication protocols, task lifecycle, quality gates
- **Safety & Guardrails** — 5-layer safety architecture, circuit breakers, incident response
- **Operational Excellence** — Cron design, heartbeat strategy, performance metrics, scaling patterns
- **Advanced Patterns** — Agent economies, consensus mechanisms, self-improving agents, disaster recovery
- **Testing** — 15 acceptance test scenarios, integration tests, 100-point quality rubric

## Install

```bash
clawhub install afrexai-agent-engineering
```

## Quick Start

```
"Design a new agent for [your purpose]"
```

The skill walks you through:
1. Agent brief (purpose, autonomy, personality)
2. Workspace file generation (all 6 core files)
3. Safety guardrails setup
4. Acceptance testing
5. Deployment and monitoring

## Who This Is For

- **Solo builders** wanting a production-quality personal assistant
- **Teams** building multi-agent workflows for business operations
- **Agencies** deploying agent systems for clients
- **Anyone** who's tired of agents that break in production

## ⚡ Level Up

This skill gives you the methodology. Want pre-built, battle-tested agent configurations for specific industries?

**AfrexAI Context Packs** ($47 each) include complete agent configs, industry-specific prompts, and operational playbooks:
- [SaaS Context Pack](https://afrexai-cto.github.io/contex...