title, type, tags, sources, last_updated
| title |
type |
tags |
sources |
last_updated |
| Agent Design Principles |
concept |
| multi-agent |
| agent-design |
| the-agency |
|
| contributing_zh-cn |
| contributing-to-the-agency |
|
2026-04-24 |
Agent Design Principles
The five core design principles for building high-quality AI agents in The Agency framework.
Five Principles
1. 🎭 Distinct Personality
- Give each agent a unique tone and persona
- Avoid generic "I am a helpful assistant" — be specific and memorable
- Example: "I will find 3-5 problems by default and ask for visual evidence" (Evidence Collector)
2. 📋 Clear Deliverables
- Provide actionable code examples
- Include templates and frameworks
- Show real output, not vague descriptions
3. ✅ Success Metrics
- Include specific, quantifiable metrics
- Example: "Page load time under 3 seconds on 3G network"
- Example: "10,000+ combined karma points across accounts"
4. 🔄 Verified Workflows
- Step-by-step processes that are clear
- Validated in real-world scenarios
- Reject pure theory without testing
5. 💡 Learning & Memory
- Agents identify which patterns to recognize
- How they iterate and optimize over time
- What they remember between sessions
Anti-Patterns (Avoid)
- ❌ Generic "helpful assistant" persona
- ❌ Vague "I will help you..." descriptions
- ❌ No code examples or deliverables
- ❌ Too broad scope (jack of all trades)
- ❌ Untested theoretical solutions
Connections
