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85f44c358a
- Remove all reverse-engineered Claude Code source code - Replace with 100% original educational content from mini-claude-code - Add clear disclaimer: independent project, not affiliated with Anthropic - 5 progressive agent implementations (v0-v4, ~1100 lines total) - Include agent-builder skill for teaching agent construction - Bilingual documentation (EN + ZH) This repository now focuses purely on teaching how modern AI agents work through original, from-scratch implementations. Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
130 lines
4.6 KiB
Markdown
130 lines
4.6 KiB
Markdown
---
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name: agent-builder
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description: |
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Design and build AI agents for any domain. Use when users:
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(1) ask to "create an agent", "build an assistant", or "design an AI system"
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(2) want to understand agent architecture, agentic patterns, or autonomous AI
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(3) need help with capabilities, subagents, planning, or skill mechanisms
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(4) ask about Claude Code, Cursor, or similar agent internals
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(5) want to build agents for business, research, creative, or operational tasks
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Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
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---
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# Agent Builder
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Build AI agents for any domain - customer service, research, operations, creative work, or specialized business processes.
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## The Core Philosophy
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> **The model already knows how to be an agent. Your job is to get out of the way.**
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An agent is not complex engineering. It's a simple loop that invites the model to act:
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```
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LOOP:
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Model sees: context + available capabilities
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Model decides: act or respond
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If act: execute capability, add result, continue
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If respond: return to user
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```
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**That's it.** The magic isn't in the code - it's in the model. Your code just provides the opportunity.
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## The Three Elements
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### 1. Capabilities (What can it DO?)
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Atomic actions the agent can perform: search, read, create, send, query, modify.
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**Design principle**: Start with 3-5 capabilities. Add more only when the agent consistently fails because a capability is missing.
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### 2. Knowledge (What does it KNOW?)
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Domain expertise injected on-demand: policies, workflows, best practices, schemas.
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**Design principle**: Make knowledge available, not mandatory. Load it when relevant, not upfront.
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### 3. Context (What has happened?)
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The conversation history - the thread connecting actions into coherent behavior.
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**Design principle**: Context is precious. Isolate noisy subtasks. Truncate verbose outputs. Protect clarity.
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## Agent Design Thinking
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Before building, understand:
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- **Purpose**: What should this agent accomplish?
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- **Domain**: What world does it operate in? (customer service, research, operations, creative...)
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- **Capabilities**: What 3-5 actions are essential?
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- **Knowledge**: What expertise does it need access to?
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- **Trust**: What decisions can you delegate to the model?
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**CRITICAL**: Trust the model. Don't over-engineer. Don't pre-specify workflows. Give it capabilities and let it reason.
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## Progressive Complexity
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Start simple. Add complexity only when real usage reveals the need:
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| Level | What to add | When to add it |
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|-------|-------------|----------------|
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| Basic | 3-5 capabilities | Always start here |
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| Planning | Progress tracking | Multi-step tasks lose coherence |
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| Subagents | Isolated child agents | Exploration pollutes context |
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| Skills | On-demand knowledge | Domain expertise needed |
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**Most agents never need to go beyond Level 2.**
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## Domain Examples
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**Business**: CRM queries, email, calendar, approvals
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**Research**: Database search, document analysis, citations
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**Operations**: Monitoring, tickets, notifications, escalation
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**Creative**: Asset generation, editing, collaboration, review
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The pattern is universal. Only the capabilities change.
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## Key Principles
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1. **The model IS the agent** - Code just runs the loop
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2. **Capabilities enable** - What it CAN do
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3. **Knowledge informs** - What it KNOWS how to do
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4. **Constraints focus** - Limits create clarity
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5. **Trust liberates** - Let the model reason
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6. **Iteration reveals** - Start minimal, evolve from usage
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## Anti-Patterns
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| Pattern | Problem | Solution |
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|---------|---------|----------|
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| Over-engineering | Complexity before need | Start simple |
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| Too many capabilities | Model confusion | 3-5 to start |
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| Rigid workflows | Can't adapt | Let model decide |
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| Front-loaded knowledge | Context bloat | Load on-demand |
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| Micromanagement | Undercuts intelligence | Trust the model |
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## Resources
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**Philosophy & Theory**:
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- `references/agent-philosophy.md` - Deep dive into why agents work
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**Implementation**:
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- `references/minimal-agent.py` - Complete working agent (~80 lines)
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- `references/tool-templates.py` - Capability definitions
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- `references/subagent-pattern.py` - Context isolation
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**Scaffolding**:
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- `scripts/init_agent.py` - Generate new agent projects
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## The Agent Mindset
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**From**: "How do I make the system do X?"
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**To**: "How do I enable the model to do X?"
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**From**: "What's the workflow for this task?"
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**To**: "What capabilities would help accomplish this?"
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The best agent code is almost boring. Simple loops. Clear capabilities. Clean context. The magic isn't in the code.
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**Give the model capabilities and knowledge. Trust it to figure out the rest.**
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