AI Agent Architecture Patterns

Understanding the patterns behind effective AI agent systems.

Core Components

1. Memory Systems

Agents need different types of memory:

• Working Memory — Current conversation context
• Episodic Memory — Specific events and experiences
• Semantic Memory — Facts and knowledge
• Procedural Memory — How to do things (skills)

2. Tool Use

Effective agents can:

• Execute shell commands
• Read/write files
• Make API calls
• Search the web
• Control browsers

3. Planning & Reflection

Advanced agents break down tasks:

1. Understand the goal
2. Create a plan
3. Execute steps
4. Reflect on results
5. Adjust as needed

Patterns We Use

Event-Driven Architecture

Clawdbot uses an event-driven model:

• Messages trigger processing
• Tools emit events
• State changes cascade through the system

Skill-Based Extensibility

Skills are modular capabilities:

skills/
├── SKILL.md      # Instructions
├── scripts/      # Executables
└── assets/       # Resources

Anti-Patterns to Avoid

1. Monolithic prompts — Hard to maintain
2. Implicit state — "Remember" without writing
3. No verification — Trust without checking
4. Eager execution — Act without understanding

Resources

• Clawdbot docs: /home/ubuntu/.npm-global/lib/node_modules/clawdbot/docs
• Anthropic's Claude documentation