# Agent Builder

> 设计并构建各类 AI 智能体/助手。适用于用户： (1) 询问“创建 agent / 助手 / 智能体系统” (2) 想理解 agent 架构、agentic 模式或自治式 AI (3) 需要能力设计、子代理、规划或 skills 机制建议 (4) 询问 Claude Code、Cursor 等智能体内部实现 (5) 想为业务/研究/创作/运营等场景构建 agent 关键词：agent, assistant, autonomous, workflow, tool use, multi-step, orchestration

- Skill: `anymouschina/agent-builder` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add anymouschina/agent-builder`
- Raw SKILL.md: https://api.skillmd.com/api/skills/anymouschina/agent-builder/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: anymouschina (https://skillmd.com/u/anymouschina)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/anymouschina/agent-builder

---


# Agent Builder

Build AI agents for any domain - customer service, research, operations, creative work, or specialized business processes.

## The Core Philosophy

> **The model already knows how to be an agent. Your job is to get out of the way.**

An agent is not complex engineering. It's a simple loop that invites the model to act:

```
LOOP:
  Model sees: context + available capabilities
  Model decides: act or respond
  If act: execute capability, add result, continue
  If respond: return to user
```

**That's it.** The magic isn't in the code - it's in the model. Your code just provides the opportunity.

## The Three Elements

### 1. Capabilities (What can it DO?)

Atomic actions the agent can perform: search, read, create, send, query, modify.

**Design principle**: Start with 3-5 capabilities. Add more only when the agent consistently fails because a capability is missing.

### 2. Knowledge (What does it KNOW?)

Domain expertise injected on-demand: policies, workflows, best practices, schemas.

**Design principle**: Make knowledge available, not mandatory. Load it when relevant, not upfront.

### 3. Context (What has happened?)

The conversation history - the thread connecting actions into coherent behavior.

**Design principle**: Context is precious. Isolate noisy subtasks. Truncate verbose outputs. Protect clarity.

## Agent Design Thinking

Before building, understand:

- **Purpose**: What should this agent accomplish?
- **Domain**: What world does it operate in? (customer service, research, operations, creative...)
- **Capabilities**: What 3-5 actions are essential?
- **Knowledge**: What expertise does it need access to?
- **Trust**: What decisions can you delegate to the model?

**CRITICAL**: Trust the model. Don't over-engineer. Don't pre-specify workflows. Give it capabilities and let it reason.

## Progressive Complexity

Start simple. Add complexity only when real usage reveals the need:

| Level | What to add | When to add it |
|-------|-------------|----------------|
| Basic | 3-5 capabilities | Always start here |
| Planning | Progress tracking | Multi-step tasks lose coherence |
| Subagents | Isolated child agents | Exploration pollutes context |
| Skills | On-demand knowledge | Domain expertise needed |

**Most agents never need to go beyond Level 2.**

## Domain Examples

**Business**: CRM queries, email, calendar, approvals
**Research**: Database search, document analysis, citations
**Operations**: Monitoring, tickets, notifications, escalation
**Creative**: Asset generation, editing, collaboration, review

The pattern is universal. Only the capabilities change.

## Key Principles

1. **The model IS the agent** - Code just runs the loop
2. **Capabilities enable** - What it CAN do
3. **Knowledge informs** - What it KNOWS how to do
4. **Constraints focus** - Limits create clarity
5. **Trust liberates** - Let the model reason
6. **Iteration reveals** - Start minimal, evolve from usage

## Anti-Patterns

| Pattern | Problem | Solution |
|---------|---------|----------|
| Over-engineering | Complexity before need | Start simple |
| Too many capabilities | Model confusion | 3-5 to start |
| Rigid workflows | Can't adapt | Let model decide |
| Front-loaded knowledge | Context bloat | Load on-demand |
| Micromanagement | Undercuts intelligence | Trust the model |

## Resources

**Philosophy & Theory**:
- `references/agent-philosophy.md` - Deep dive into why agents work

**Implementation**:
- `references/minimal-agent.py` - Complete working agent (~80 lines)
- `references/tool-templates.py` - Capability definitions
- `references/subagent-pattern.py` - Context isolation

**Scaffolding**:
- `scripts/init_agent.py` - Generate new agent projects

## The Agent Mindset

**From**: "How do I make the system do X?"
**To**: "How do I enable the model to do X?"

**From**: "What's the workflow for this task?"
**To**: "What capabilities would help accomplish this?"

The best agent code is almost boring. Simple loops. Clear capabilities. Clean context. The magic isn't in the code.

**Give the model capabilities and knowledge. Trust it to figure out the rest.**