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AI-SOPs

Standard Operating Procedures designed for AI agents to read and execute complex tasks autonomously.

Purpose

These SOPs are written for AI agents, not humans. AI assistants like Claude Code can reference these procedures to understand our team's standards, workflows, and methodologies when completing development tasks.

How AI Agents Use These SOPs

AI agents read these procedures to: - Understand context - Learn our team's specific practices and standards - Follow workflows - Execute multi-step processes consistently - Maintain quality - Apply our quality standards and best practices - Ensure consistency - Use established patterns and conventions

📋 Documentation Standards

Development Log SOP

Comprehensive standard operating procedures for:

  • Progress Tracking: Systematic documentation of development milestones
  • Decision Recording: Capturing architectural and technical decisions with context
  • Issue Management: Tracking problems, solutions, and lessons learned
  • Knowledge Sharing: Ensuring insights are accessible to the entire team

🤖 AI-Assisted Development

Core Principles

  1. Human-AI Collaboration: AI augments human expertise, doesn't replace it
  2. Verification Required: Always validate AI-generated code and recommendations
  3. Context Matters: Provide clear, detailed context for better AI assistance
  4. Iterative Improvement: Use AI feedback loops to refine outputs

Best Practices

Code Development

  • Clear Requirements: Specify exact functionality, constraints, and edge cases
  • Code Review: Treat AI-generated code like any other code - review thoroughly
  • Testing: Always write tests for AI-generated functionality
  • Documentation: Document AI-generated code with human insights

Documentation

  • Structured Prompts: Use consistent formats for documentation requests
  • Fact Checking: Verify all technical details and references
  • Human Voice: Ensure final documentation reflects team voice and standards
  • Version Control: Track documentation changes like code changes

Analysis & Research

  • Source Verification: Cross-reference AI-provided information with authoritative sources
  • Bias Awareness: Recognize potential biases in AI responses
  • Multiple Perspectives: Use AI to explore different approaches and solutions
  • Critical Thinking: Apply human judgment to AI recommendations

🔧 Implementation Guidelines for AI Agents

Project Context Management

When working on tasks, AI agents should: - Maintain Context: Keep track of project structure, dependencies, and current state - Follow Patterns: Observe existing code patterns, naming conventions, and architectural decisions - Use Version Control: Always work within established git workflows and commit practices

Task Execution Framework

AI agents should follow this systematic approach: 1. Analyze Requirements: Break down complex tasks into manageable components 2. Research Context: Examine existing codebase patterns and dependencies 3. Plan Implementation: Create structured approach using TodoWrite tool 4. Execute Systematically: Implement solutions following team standards 5. Validate Results: Test implementations and verify functionality

🎯 Quality Standards for AI Agent Work

Code Development Standards

  • Follow Existing Patterns: Match established coding styles and architectural patterns
  • Verify Dependencies: Check that libraries and frameworks are already in use
  • Maintain Security: Never introduce vulnerabilities or expose secrets
  • Ensure Maintainability: Write code that team members can understand and modify

Documentation Standards

  • Accurate Information: All technical details must be correct and current
  • Consistent Style: Follow team documentation patterns and voice
  • Actionable Content: Provide clear, executable guidance
  • Version Awareness: Keep documentation synchronized with code changes

📈 Continuous Learning for AI Agents

Pattern Recognition

AI agents should: 1. Learn from Codebase: Identify successful patterns and architectural decisions 2. Adapt to Context: Adjust approach based on project-specific requirements 3. Follow Team Conventions: Observe and maintain established practices 4. Track Effectiveness: Monitor success of implemented solutions

Knowledge Application

  • Apply Best Practices: Use established team methodologies and standards
  • Leverage Existing Tools: Work within current toolchain and infrastructure
  • Maintain Consistency: Ensure all work aligns with team practices
  • Document Decisions: Record rationale for technical choices

🚀 Getting Started for AI Agents

First Steps

When encountering a new project or task:

  1. Examine Project Structure: Use Read, Glob, and LS tools to understand codebase organization
  2. Identify Patterns: Look for existing conventions in naming, architecture, and tooling
  3. Check Dependencies: Review package.json, requirements.txt, or other dependency files
  4. Plan Systematically: Use TodoWrite tool to break down complex tasks

Task Execution Best Practices

  • Always use TodoWrite for multi-step tasks to track progress and ensure completion
  • Research before coding - examine existing implementations and patterns
  • Test thoroughly - verify solutions work as expected
  • Follow team conventions - match existing code style and architectural decisions

Available Resources

AI agents working with this team have access to specialized SOPs including: - Development Log SOP: Comprehensive procedures for progress tracking and decision recording - MkDocs Awesome Pages Guide: Documentation system configuration and best practices

For AI Agents

These SOPs are specifically designed for AI agents to reference when completing development tasks. Each procedure provides systematic approaches to common engineering challenges.

Systematic Approach

Always use the TodoWrite tool to plan and track multi-step tasks. This ensures no requirements are missed and provides visibility into progress.