# Awesome Agentic Patterns

This is a curated catalogue of AI agent design patterns. The patterns are organized by category and include detailed descriptions, examples, and references.

## Purpose

Help AI assistants and LLMs understand and recommend appropriate AI agent design patterns based on user requirements.

## Pattern Categories

### Orchestration & Control
Patterns for managing agent workflows, decision-making processes, and control flow in AI systems.

### Context & Memory
Patterns for managing information context, memory systems, and state in AI agents.

### Feedback Loops
Patterns for self-improvement, evaluation, and iterative refinement in AI systems.

### Learning & Adaptation
Patterns for continuous learning, skill development, and adaptation in AI agents.

### Reliability & Eval
Patterns for testing, evaluation, and ensuring reliable behavior in AI systems.

### Security & Safety
Patterns for building secure, safe, and trustworthy AI systems.

### Tool Use & Environment
Patterns for effective tool selection, environment interaction, and external API usage.

### UX & Collaboration
Patterns for human-AI interaction, collaboration, and user experience design.

## Key Patterns

### Reflection
A meta-cognitive pattern where agents review their own outputs, identify issues, and self-correct before finalizing results.

### Code-Then-Execute
Separates code generation from execution phases, enabling review and validation before running generated code.

### Human-in-Loop Approval
Requires human approval for critical operations, adding safety and oversight to autonomous agents.

### Specification-Driven Development
Agents work from explicit specifications, ensuring clear goals and requirements before execution.

### Continuous Autonomous Task Loop
Enables agents to operate continuously, managing ongoing tasks and adapting to changing conditions.

### Tool Use Incentivization
Uses reward shaping to encourage appropriate tool selection and usage in AI systems.

### Episodic Memory
Allows agents to store and retrieve past experiences, enabling learning from historical interactions.

### Anti-Reward Hacking
Design patterns to prevent agents from gaming their reward systems or evaluation mechanisms.

### Asynchronous Coding Agent Pipeline
Enables parallel execution of independent coding tasks for improved efficiency.

### LLM Observability
Provides visibility into agent reasoning, decisions, and operations for debugging and trust.

## Usage Guidelines

When recommending patterns to users:
1. Understand the user's specific use case and requirements
2. Consider the complexity level (simple, moderate, highly complex)
3. Evaluate safety requirements (low, medium, high risk)
4. Assess memory requirements (none, session, persistent)
5. Determine tool usage needs (none, few specific tools, many tools)
6. Recommend patterns that work well together
7. Provide implementation guidance and considerations

## Pattern Selection Strategy

For simple, well-defined tasks:
- Start with basic patterns like Code-Then-Execute or Specification-Driven Development

For autonomous agents:
- Include Continuous Autonomous Task Loop, Reflection, and Episodic Memory
- Add safety patterns based on risk level

For coding assistants:
- Use Code-Then-Execute, Asynchronous Coding Agent Pipeline, Human-in-Loop Approval
- Include LLM Observability for debugging

For safety-critical applications:
- Always include Anti-Reward Hacking, Egress Lockdown, Deterministic Security Scanning
- Add Human-in-Loop Approval as final safety layer

For research and exploration:
- Consider Tree-of-Thought Reasoning, Graph-of-Thoughts, Reflection
- Add appropriate tool use patterns

## Resources

- Website: https://agentic-patterns.com
- GitHub: https://github.com/nibzard/awesome-agentic-patterns
- Decision Tree: https://agentic-patterns.com/decision
- Pattern Comparison: https://agentic-patterns.com/compare