agent-patterns

name	agent-patterns
description	AI agents are autonomous systems that use Large Language Models (LLMs) Use when this capability is needed.
metadata	{"author":"amnadtaowsoam"}

Agent Patterns

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

AI agents are autonomous systems that use Large Language Models (LLMs) to reason, plan, and execute tasks. Unlike simple chatbots, agents can break down complex problems into steps, use tools to interact with external systems, and adapt their behavior based on feedback. This skill covers agent architectures (ReAct, Plan-and-Execute, Tree of Thoughts, Reflexion), core components (memory, planning, tool use), frameworks (LangChain, AutoGPT, CrewAI, Semantic Kernel), tool design, multi-agent systems, error recovery, guardrails, evaluation, and production deployment.

Why This Matters

AI agents represent a paradigm shift from passive LLM interactions to autonomous problem-solving systems. They enable:

• Complex Task Decomposition: Breaking down user goals into actionable steps
• Tool Integration: Seamlessly connecting LLMs to external APIs and databases
• Adaptive Behavior: Learning from feedback and adjusting strategies
• Multi-Agent Collaboration: Specialized agents working together on complex problems

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

Skill Composition

• Depends on: None
• Compatible with: None
• Conflicts with: None
• Related Skills: None

Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions

• LLM API is available and reliable
• Tools are well-documented with clear schemas
• Network connectivity for external API calls
• Sufficient compute resources for LLM inference

Compatibility

• Python 3.8+
• OpenAI API (or compatible LLM provider)
• LangChain 0.1+ (optional, for framework-based agents)
• CrewAI 0.1+ (optional, for multi-agent systems)

Test Scenario Matrix (QA Strategy)

Type	Focus Area	Required Scenarios / Mocks
Unit	Core Logic	Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage
Integration	DB / API	All external API calls or database connections must be mocked during unit tests
E2E	User Journey	Critical user flows to test
Performance	Latency / Load	Benchmark requirements
Security	Vuln / Auth	SAST/DAST or dependency audit
Frontend	UX / A11y	Accessibility checklist (WCAG), Performance Budget (Lighthouse score)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns

1. Unbounded Iterations: Agents running forever without iteration limits
2. Tool Overload: Too many tools causing confusion and poor performance
3. Memory Bloat: Unbounded memory growth without cleanup
4. Prompt Injection: User input manipulating agent behavior
5. Silent Failures: Tool errors not properly propagated
6. Hard-coded Context: Context baked into prompts instead of using memory

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure

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