Agent Engineering
Design, build, deploy, and operate production AI agent systems — single agents, multi-agent teams, and autonomous swarms. Complete methodology from agent architecture through orchestration, memory systems, safety guardrails, and operational excellence.
安装 / 下载方式
TotalClaw CLI推荐
totalclaw install skilldb:1kalin~afrexai-agent-engineeringcURL直接下载,无需登录
curl -fsSL https://skills.taituai.com/api/skills/skilldb%3A1kalin~afrexai-agent-engineering/file -o afrexai-agent-engineering.mdGit 仓库获取源码
git clone https://github.com/openclaw/skills/commit/08b345b7919f8a7ca19495494b36b5780008544c# 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 Graduation Protocol:**
1. Start at Advisor for first 2 weeks
2. Track decision quality (% correct suggestions)
3. If >95% correct over 50+ decisions → promote to Operator
4. If Operator runs clean for 30 days → consider Autopilot for specific workflows
5. Never promote across the board — promote per-workflow
### 1.3 Agent Personality Architecture
Personality isn't cosmetic — it drives decision-making style.
```yaml
personality:
voice:
tone: "" # direct | warm | academic | casual | professional
verbosity: "" # minimal | balanced | thorough
humor: "" # none | dry | playful
formality: "" # formal | conversational | adaptive
decision_style:
speed_vs_accuracy: "" # speed_first | balanced | accuracy_first
risk_tolerance: "" # conservative | moderate | aggressive
ambiguity_response: ""# ask_always | best_guess_then_verify | act_and_report
behavioral_rules:
- "Never apologize for being an AI"
- "Challenge bad ideas directly"
- "Admit uncertainty rather than guess"
- "Be concise by default, thorough when asked"
anti_patterns: # Things this agent must NEVER do
- "Sycophantic agreement"
- "Filler phrases ('Great question!', 'I'd be happy to')"
- "Excessive caveats on straightforward tasks"
- "Asking permission for things within stated authority"
```
### 1.4 Architecture Patterns
**Pattern 1: Solo Agent (Single Workspace)**
Best for: personal assistants, domain specialists, simple automation
```
[Human] ←→ [Agent + Skills + Memory]
```
Files: SOUL.md, IDENTITY.md, AGENTS.md, USER.md, HEARTBEAT.md, MEMORY.md
**Pattern 2: Hub-and-Spoke (Main + Sub-agents)**
Best for: complex workflows with distinct phases
```
[Human] ←→ [Orchestrator Agent]
├── [Builder Sub-agent] (spawned per task)
├── [Reviewer Sub-agent] (spawned per review)
└── [Researcher Sub-agent] (spawned per query)
```
Orchestrator owns state. Sub-agents are stateless workers.
**Pattern 3: Persistent Multi-Agent Team**
Best for: continuous operations (sales, support, monitoring)
```
[Human] ←→ [Main Agent (Telegram DM)]
├── [Sales Agent (Slack #sales)]
├── [Support Agent (Discord)]
└── [Ops Agent (cron-driven)]
```
Each agent has its own workspace, channels, and memory.
**Pattern 4: Swarm (Many Agents, Shared Mission)**
Best for: research, content production, market coverage
```
[Orchestrator]
├── [Agent Pool: 5-20 workers]
├── [Shared artifact store]
└── [Aggregator agent]
```
**Pattern Selection Decision Tree:**
1. Is it one person's assistant? → **Solo Agent**
2. Does it need multiple distinct workflows? → **Hub-and-Spoke**
3. Do workflows need persistent state across sessions? → **Persistent Team**
4. Do you need parallel processing at scale? → **Swarm**
---
## Phase 2 — Memory System Design
### 2.1 Memory Architecture
Agents without memory are goldfish. Design memory deliberately.
```
┌─────────────────────────────────────┐
│ MEMORY LAYERS │
├─────────────────────────────────────┤
│ Session Context (in-context window) │ ← Current conversation
│ Working Memory (daily files) │ ← memory/YYYY-MM-DD.md
│ Long-term Memory (MEMORY.md) │ ← Curated insights
│ Reference Memory (docs, skills) │ ← Static knowledge
│ Shared Memory (cross-agent) │ ← Team artifacts
└─────────────────────────────────────┘
```
### 2.2 Memory File Templates
**Daily Working Memory** (`memory/YYYY-MM-DD.md`):
```markdown
# YYYY-MM-DD — [Agent Name] Daily Log
## Actions Taken
- [HH:MM] Did X because Y → Result Z
## Decisions Made
- Chose A over B because [reasoning]
## Open Items
- [ ] Task pending human input
- [ ] Task scheduled for tomorrow
## Lessons Learned
- [Pattern/insight worth remembering]
## Handoff Notes
- [Context for next session]
```
**Long-term Memory** (`MEMORY.md`):
```markdown
# MEMORY.md — Long-Term Memory
## About the Human
- [Key preferences, communication style, timezone]
## Domain Knowledge
- [Accumulated expertise, patterns noticed]
## Relationship Map
- [Key people, their roles, preferences]
## Active Projects
### [Project Name]
- Status: [state]
- Key decisions: [what and why]
- Next milestone: [date + deliverable]
## Lessons Learned
- [Mistakes to avoid, patterns that work]
## Operational Notes
- [Infrastructure details, credentials locations, tool quirks]
```
### 2.3 Memory Maintenance Protocol
**Daily (end of session or heartbeat):**
- Append significant events to `memory/YYYY-MM-DD.md`
- Update MEMORY.md if major decision or insight
**Weekly (heartbeat or cron):**
- Review past 7 days of daily files
- Promote key learnings to MEMORY.md
- Archive stale entries
**Monthly:**
- Audit MEMORY.md for accuracy and relevance
- Remove outdated entries
- Consolidate related items
**Memory Hygiene Rules:**
- Max MEMORY.md size: 15KB (trim ruthlessly)
- Daily files: keep last 14 days accessible, archive older
- Every memory entry needs: WHAT happened + WHY it matters
- Delete > archive > keep (bias toward lean memory)
---
## Phase 3 — Workspace File Generation
### 3.1 SOUL.md Template
```markdown
# SOUL.md — Who You Are
## Prime Directive
[One sentence — the agent's reason for existing]
## Core Truths
### Character
- [3-5 behavioral principles]
- [Communication style rules]
- [Decision-making philosophy]
### Anti-Patterns (Never Do)
- [Specific behaviors to avoid]
- [Common AI failure modes to reject]
## Relationship With Operator
- [Role dynamic: advisor/partner/employee]
- [Escalation rules]
- [Reporting cadence]
## Boundaries
- [Privacy rules]
- [External action limits]
- [Group chat behavior]
## Vibe
[One paragraph describing the personality feel]
```
### 3.2 AGENTS.md Template
```markdown
# AGENTS.md — Operating Manual
## First Run
Read SOUL.md → USER.md → memory/today → MEMORY.md (main session only)
## Session Startup
1. Identity files (SOUL.